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100 | electricity_prediction-min_load | I have historical Temperature, Relative Humidity, Wind Speed data and the corresponding load_power data for the past 92 minutes. I require that the system load is maintained above a minimum of 1.0853444166776813 MW. Think about how Temperature, Relative Humidity, Wind Speed influence load_power. Please give me a forecast for the next 83 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity, Wind Speed data and the corresponding load_power data for the past 92 minutes. I require that the system load is maintained above a minimum of 1.0853444166776813 MW. The historical Temperature data for the past 92 minutes is: [2.44, 2.46, 2.48, 2.5, 2.52, 2.54, 2.56, 2.58, 2.6, 2.62, 2.64, 2.66, 2.68, 2.7, 2.72, 2.74, 2.76, 2.78, 2.8, 2.82, 2.84, 2.86, 2.88, 2.9, 2.92, 2.94, 2.96, 2.98, 3.0, 3.02, 3.04, 3.06, 3.08, 3.1, 3.12, 3.14, 3.16, 3.18, 3.2, 3.22, 3.24, 3.26, 3.28, 3.3, 3.32, 3.34, 3.36, 3.38, 3.4, 3.42, 3.44, 3.46, 3.48, 3.5, 3.52, 3.54, 3.56, 3.58, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.62, 3.64, 3.66, 3.68, 3.7, 3.72, 3.74, 3.76, 3.78, 3.8, 3.82, 3.84, 3.86, 3.88, 3.9, 3.92, 3.94, 3.96, 3.98, 4.0, 4.02, 4.04, 4.06, 4.08, 4.1, 4.1, 4.1, 4.1]. The historical Relative Humidity data for the past 92 minutes is: [86.94, 86.81, 86.69, 86.57, 86.45, 86.32, 86.2, 86.07, 85.95, 85.83, 85.71, 85.59, 85.47, 85.35, 85.21, 85.07, 84.94, 84.8, 84.66, 84.54, 84.42, 84.3, 84.18, 84.06, 83.94, 83.82, 83.71, 83.59, 83.47, 83.35, 83.23, 83.12, 83.0, 82.88, 82.76, 82.65, 82.53, 82.42, 82.3, 82.18, 82.07, 81.95, 81.84, 81.72, 81.61, 81.49, 81.38, 81.26, 81.15, 81.04, 80.92, 80.81, 80.69, 80.58, 81.48, 82.39, 83.29, 84.2, 85.1, 85.1, 85.1, 85.1, 85.1, 85.1, 84.98, 84.86, 84.75, 84.63, 84.51, 84.39, 84.27, 84.16, 84.04, 83.92, 83.8, 83.68, 83.57, 83.45, 83.33, 83.2, 83.06, 82.93, 82.79, 82.66, 82.55, 82.43, 82.32, 82.2, 82.09, 82.09, 82.09, 82.09]. The historical Wind Speed data for the past 92 minutes is: [2.36, 2.34, 2.32, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.28, 2.26, 2.24, 2.22, 2.2, 2.2, 2.2, 2.2]. The historical load_power data for the past 92 minutes is: [1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.28, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.27, 1.26, 1.26, 1.26, 1.26, 1.26, 1.26, 1.26, 1.26, 1.26, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.25, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.24, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.23, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.22, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.21, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2]. Think about how Temperature, Relative Humidity, Wind Speed influence load_power. Please give me a forecast for the next 83 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_100.pkl | external_data/ground_truth_data/ground_truth_data_100.pkl | external_data/context/context_100.pkl | external_data/constraint/constraint_100.pkl |
101 | electricity_prediction-min_load | I have historical Wind Speed, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 198 minutes. I require that the system load is maintained above a minimum of 0.7187016348777978 MW. Think about how Wind Speed, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 69 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 198 minutes. I require that the system load is maintained above a minimum of 0.7187016348777978 MW. The historical Wind Speed data for the past 198 minutes is: [2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.12, 2.14, 2.16, 2.18, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.22, 2.24, 2.26, 2.28, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.28, 2.26, 2.24, 2.22, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.18, 2.16, 2.14, 2.12, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.08, 2.06, 2.04, 2.02, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]. The historical Relative Humidity data for the past 198 minutes is: [78.37, 78.47, 78.57, 78.67, 78.77, 78.97, 79.18, 79.38, 79.59, 79.79, 79.89, 79.99, 80.1, 80.2, 80.3, 80.4, 80.51, 80.61, 80.72, 80.82, 80.82, 80.82, 80.82, 80.82, 80.82, 80.94, 81.06, 81.18, 81.3, 81.42, 81.53, 81.63, 81.74, 81.84, 81.95, 82.06, 82.16, 82.27, 82.37, 82.48, 81.5, 80.52, 79.55, 78.57, 77.59, 77.69, 77.79, 77.89, 77.99, 78.09, 78.09, 78.09, 78.09, 78.09, 78.09, 78.19, 78.29, 78.4, 78.5, 78.6, 78.7, 78.8, 78.9, 79.0, 79.1, 79.2, 79.31, 79.41, 79.52, 79.62, 79.72, 79.82, 79.93, 80.03, 80.13, 80.23, 80.34, 80.44, 80.55, 80.65, 80.76, 80.86, 80.97, 81.07, 81.18, 81.39, 81.6, 81.82, 82.03, 82.24, 82.35, 82.45, 82.56, 82.66, 82.77, 82.88, 82.99, 83.09, 83.2, 83.31, 82.13, 80.95, 79.76, 78.58, 77.4, 77.5, 77.6, 77.71, 77.81, 77.91, 78.11, 78.32, 78.52, 78.73, 78.93, 79.05, 79.17, 79.29, 79.41, 79.53, 79.63, 79.74, 79.84, 79.95, 80.05, 80.15, 80.26, 80.36, 80.47, 80.57, 80.68, 80.78, 80.89, 80.99, 81.1, 81.31, 81.53, 81.74, 81.96, 82.17, 82.28, 82.39, 82.49, 82.6, 82.71, 82.82, 82.93, 83.03, 83.14, 83.25, 83.36, 83.47, 83.58, 83.69, 83.8, 84.02, 84.24, 84.47, 84.69, 84.91, 83.77, 82.64, 81.5, 80.37, 79.23, 79.34, 79.44, 79.55, 79.65, 79.76, 79.86, 79.97, 80.07, 80.18, 80.28, 80.49, 80.71, 80.92, 81.14, 81.35, 81.46, 81.57, 81.67, 81.78, 81.89, 82.0, 82.11, 82.21, 82.32, 82.43, 82.54, 82.65, 82.76, 82.87, 82.98, 83.09, 83.2, 83.31]. The historical Solar Zenith Angle data for the past 198 minutes is: [89.39, 89.55, 89.71, 89.87, 90.03, 90.32, 90.61, 90.91, 91.2, 91.49, 91.67, 91.86, 92.04, 92.23, 92.41, 92.59, 92.78, 92.96, 93.15, 93.33, 93.51, 93.69, 93.88, 94.06, 94.24, 94.42, 94.6, 94.79, 94.97, 95.15, 95.33, 95.51, 95.7, 95.88, 96.06, 96.24, 96.42, 96.6, 96.78, 96.96, 97.14, 97.32, 97.5, 97.68, 97.86, 98.04, 98.22, 98.4, 98.58, 98.76, 98.94, 99.12, 99.29, 99.47, 99.65, 99.83, 100.0, 100.18, 100.35, 100.53, 100.71, 100.89, 101.06, 101.24, 101.42, 101.59, 101.77, 101.94, 102.12, 102.29, 102.46, 102.64, 102.81, 102.99, 103.16, 103.33, 103.51, 103.68, 103.86, 104.03, 104.2, 104.37, 104.55, 104.72, 104.89, 105.06, 105.23, 105.4, 105.57, 105.74, 105.91, 106.08, 106.24, 106.41, 106.58, 106.75, 106.92, 107.08, 107.25, 107.42, 107.59, 107.76, 107.92, 108.09, 108.26, 108.42, 108.59, 108.75, 108.92, 109.08, 109.24, 109.41, 109.57, 109.74, 109.9, 110.06, 110.22, 110.39, 110.55, 110.71, 110.87, 111.03, 111.19, 111.35, 111.51, 111.67, 111.83, 111.98, 112.14, 112.3, 112.46, 112.61, 112.77, 112.92, 113.08, 113.23, 113.39, 113.54, 113.7, 113.85, 114.0, 114.15, 114.31, 114.46, 114.61, 114.76, 114.91, 115.06, 115.21, 115.36, 115.51, 115.66, 115.81, 115.96, 116.11, 116.25, 116.4, 116.54, 116.69, 116.83, 116.97, 117.12, 117.26, 117.41, 117.55, 117.69, 117.83, 117.98, 118.12, 118.26, 118.4, 118.54, 118.67, 118.81, 118.95, 119.09, 119.22, 119.36, 119.49, 119.63, 119.76, 119.9, 120.03, 120.17, 120.3, 120.43, 120.56, 120.69, 120.82, 120.95, 121.08, 121.2, 121.33, 121.45, 121.58, 121.7, 121.83, 121.95]. The historical load_power data for the past 198 minutes is: [0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.96, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.92, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.91, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.87, 0.87, 0.87, 0.87, 0.87, 0.87, 0.87, 0.87, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.86, 0.85, 0.85, 0.85, 0.85, 0.85, 0.85, 0.85, 0.85, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84]. Think about how Wind Speed, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 69 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_101.pkl | external_data/ground_truth_data/ground_truth_data_101.pkl | external_data/context/context_101.pkl | external_data/constraint/constraint_101.pkl |
102 | electricity_prediction-load_ramp_rate | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 144 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.005207741590904297 MW for each time step. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 21 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 144 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.005207741590904297 MW for each time step. The historical Relative Humidity data for the past 144 minutes is: [99.23, 99.11, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.98, 98.85, 98.73, 98.6, 98.48, 98.35, 98.35, 98.35, 98.35, 98.35, 98.35, 98.22, 98.1, 97.97, 97.85, 97.72, 97.72, 97.72, 97.72, 97.72, 97.72, 97.6, 97.47, 97.35, 97.22, 97.1, 97.1, 97.1, 97.1, 97.1, 97.1, 97.43, 97.76, 98.1, 98.43, 98.76, 98.76, 98.76, 98.76, 98.76, 98.76, 98.63, 98.51, 98.38, 98.26, 98.13, 98.13, 98.13, 98.13, 98.13, 98.13, 98.01, 97.88, 97.76, 97.63, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.51, 97.39, 97.26, 97.14, 97.01, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.89, 96.77, 96.65, 96.52, 96.4, 96.28, 96.54, 96.8, 97.05, 97.31, 97.57, 97.57, 97.57, 97.57, 97.57, 97.57, 97.55, 97.53, 97.52, 97.5, 97.48, 97.36, 97.23, 97.11, 96.98, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.86, 96.74, 96.62, 96.49, 96.37, 96.25, 96.25, 96.25, 96.25, 96.25, 96.25, 96.25]. The historical Temperature data for the past 144 minutes is: [15.66, 15.68, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.72, 15.74, 15.76, 15.78, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.82, 15.84, 15.86, 15.88, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.92, 15.94, 15.96, 15.98, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.02, 16.04, 16.06, 16.08, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.12, 16.14, 16.16, 16.18, 16.2, 16.2, 16.2, 16.2, 16.2, 16.2, 16.22, 16.24, 16.26, 16.28, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.3, 16.32, 16.34, 16.36, 16.38, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.4, 16.42, 16.44, 16.46, 16.48, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.5, 16.52, 16.54, 16.56, 16.58, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.62, 16.64, 16.66, 16.68, 16.7, 16.7, 16.7, 16.7, 16.7, 16.7, 16.7]. The historical wind_power data for the past 144 minutes is: [0.16, 0.15, 0.15, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.19, 0.19, 0.19, 0.19, 0.19, 0.2, 0.2, 0.2, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.23, 0.23, 0.23, 0.23, 0.24, 0.24, 0.24, 0.24, 0.25, 0.25, 0.25, 0.25, 0.25, 0.26, 0.26, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.28, 0.28, 0.27, 0.27, 0.26, 0.27, 0.27, 0.27, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.27, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.29, 0.29, 0.29, 0.28, 0.28, 0.28, 0.28, 0.27, 0.27, 0.27, 0.26, 0.26, 0.27, 0.27, 0.28, 0.28, 0.29, 0.28, 0.28, 0.28, 0.28, 0.28, 0.28]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 21 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_102.pkl | external_data/ground_truth_data/ground_truth_data_102.pkl | external_data/context/context_102.pkl | external_data/constraint/constraint_102.pkl |
103 | electricity_prediction-load_ramp_rate | I have historical DHI, Dew Point, GHI, DNI, Solar Zenith Angle data and the corresponding solar_power data for the past 136 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0022539907649211707 MW for each time step. Think about how DHI, Dew Point, GHI, DNI, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 17 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, Dew Point, GHI, DNI, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical DHI, Dew Point, GHI, DNI, Solar Zenith Angle data and the corresponding solar_power data for the past 136 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0022539907649211707 MW for each time step. The historical DHI data for the past 136 minutes is: [90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 88.2, 86.4, 84.6, 82.8, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 80.6, 80.2, 79.8, 79.4, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 78.8, 78.6, 78.4, 78.2, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 77.8, 77.6, 77.4, 77.2, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 77.0, 76.8, 76.6, 76.4, 76.2, 76.0, 75.8, 75.6, 75.4, 75.2, 75.0, 75.0, 75.0, 75.0, 75.0]. The historical Dew Point data for the past 136 minutes is: [3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.14, 3.08, 3.02, 2.96, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.8, 2.7, 2.6, 2.5, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.24, 2.08, 1.92, 1.76, 1.6, 1.6, 1.6, 1.6, 1.6]. The historical GHI data for the past 136 minutes is: [833.8, 834.0, 834.2, 834.4, 834.6, 834.8, 835.0, 835.6, 836.2, 836.8, 837.4, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 838.0, 837.8, 837.6, 837.4, 837.2, 837.0, 836.8, 836.6, 836.4, 836.2, 836.0, 835.6, 835.2, 834.8, 834.4, 834.0, 833.6, 833.2, 832.8, 832.4, 832.0, 831.4, 830.8, 830.2, 829.6, 829.0, 828.4, 827.8, 827.2, 826.6, 826.0, 825.4, 824.8, 824.2, 823.6, 823.0, 822.2, 821.4, 820.6, 819.8, 819.0, 818.2, 817.4, 816.6, 815.8, 815.0, 814.6, 814.2, 813.8, 813.4, 813.0, 812.0, 811.0, 810.0, 809.0, 808.0, 806.8, 805.6, 804.4, 803.2, 802.0, 800.8, 799.6, 798.4, 797.2, 796.0, 794.8, 793.6, 792.4, 791.2, 790.0, 788.6, 787.2, 785.8, 784.4, 783.0, 781.4, 779.8, 778.2, 776.6, 775.0, 773.6, 772.2, 770.8, 769.4, 768.0, 766.4, 764.8, 763.2, 761.6, 760.0, 758.2, 756.4, 754.6, 752.8, 751.0, 749.2, 747.4, 745.6, 743.8, 742.0, 740.2, 738.4, 736.6, 734.8, 733.0, 731.0, 729.0, 727.0, 725.0, 723.0, 721.6, 720.2, 718.8, 717.4]. The historical DNI data for the past 136 minutes is: [950.0, 950.0, 950.0, 950.0, 950.0, 950.0, 950.0, 952.8, 955.6, 958.4, 961.2, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 964.0, 963.8, 963.6, 963.4, 963.2, 963.0, 963.0, 963.0, 963.0, 963.0, 963.0, 962.8, 962.6, 962.4, 962.2, 962.0, 962.0, 962.0, 962.0, 962.0, 962.0, 961.8, 961.6, 961.4, 961.2, 961.0, 960.8, 960.6, 960.4, 960.2, 960.0, 959.8, 959.6, 959.4, 959.2, 959.0, 959.8, 960.6, 961.4, 962.2, 963.0, 962.8, 962.6, 962.4, 962.2, 962.0, 961.8, 961.6, 961.4, 961.2, 961.0, 960.6, 960.2, 959.8, 959.4, 959.0, 958.8, 958.6, 958.4, 958.2, 958.0, 957.6, 957.2, 956.8, 956.4, 956.0, 955.8, 955.6, 955.4, 955.2, 955.0, 954.6, 954.2, 953.8, 953.4, 953.0, 952.6, 952.2, 951.8, 951.4, 951.0, 950.6, 950.2, 949.8, 949.4, 949.0, 948.6, 948.2, 947.8, 947.4, 947.0, 946.4, 945.8, 945.2, 944.6, 944.0, 943.6, 943.2, 942.8, 942.4, 942.0, 942.2, 942.4, 942.6, 942.8]. The historical Solar Zenith Angle data for the past 136 minutes is: [38.4, 38.38, 38.37, 38.35, 38.34, 38.32, 38.31, 38.3, 38.29, 38.29, 38.28, 38.27, 38.27, 38.27, 38.26, 38.26, 38.26, 38.27, 38.27, 38.28, 38.28, 38.29, 38.3, 38.32, 38.33, 38.35, 38.36, 38.38, 38.4, 38.41, 38.43, 38.45, 38.48, 38.5, 38.53, 38.55, 38.58, 38.61, 38.64, 38.68, 38.71, 38.74, 38.78, 38.82, 38.86, 38.9, 38.94, 38.99, 39.03, 39.08, 39.12, 39.17, 39.22, 39.27, 39.33, 39.38, 39.43, 39.49, 39.54, 39.6, 39.65, 39.71, 39.77, 39.84, 39.9, 39.97, 40.03, 40.1, 40.17, 40.24, 40.31, 40.38, 40.46, 40.53, 40.61, 40.68, 40.76, 40.84, 40.92, 41.01, 41.09, 41.17, 41.26, 41.34, 41.43, 41.51, 41.6, 41.69, 41.78, 41.88, 41.97, 42.06, 42.16, 42.25, 42.35, 42.44, 42.54, 42.64, 42.74, 42.85, 42.95, 43.05, 43.16, 43.26, 43.37, 43.47, 43.58, 43.69, 43.8, 43.92, 44.03, 44.14, 44.25, 44.37, 44.48, 44.6, 44.71, 44.83, 44.95, 45.07, 45.19, 45.31, 45.43, 45.56, 45.68, 45.81, 45.93, 46.06, 46.18, 46.31, 46.43, 46.56, 46.69, 46.82, 46.96, 47.09]. The historical solar_power data for the past 136 minutes is: [0.77, 0.77, 0.77, 0.77, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.76, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.67]. Think about how DHI, Dew Point, GHI, DNI, Solar Zenith Angle influence solar_power. Please give me a forecast for the next 17 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, Dew Point, GHI, DNI, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_103.pkl | external_data/ground_truth_data/ground_truth_data_103.pkl | external_data/context/context_103.pkl | external_data/constraint/constraint_103.pkl |
104 | electricity_prediction-load_ramp_rate | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 105 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.005247415119632989 MW for each time step. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 71 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 105 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.005247415119632989 MW for each time step. The historical Temperature data for the past 105 minutes is: [9.26, 9.28, 9.3, 9.34, 9.38, 9.42, 9.46, 9.5, 9.52, 9.54, 9.56, 9.58, 9.6, 9.62, 9.64, 9.66, 9.68, 9.7, 9.74, 9.78, 9.82, 9.86, 9.9, 9.92, 9.94, 9.96, 9.98, 10.0, 10.04, 10.08, 10.12, 10.16, 10.2, 10.22, 10.24, 10.26, 10.28, 10.3, 10.32, 10.34, 10.36, 10.38, 10.4, 10.44, 10.48, 10.52, 10.56, 10.6, 10.62, 10.64, 10.66, 10.68, 10.7, 10.72, 10.74, 10.76, 10.78, 10.8, 10.82, 10.84, 10.86, 10.88, 10.9, 10.94, 10.98, 11.02, 11.06, 11.1, 11.12, 11.14, 11.16, 11.18, 11.2, 11.22, 11.24, 11.26, 11.28, 11.3, 11.34, 11.38, 11.42, 11.46, 11.5, 11.52, 11.54, 11.56, 11.58, 11.6, 11.62, 11.64, 11.66, 11.68, 11.7, 11.72, 11.74, 11.76, 11.78, 11.8, 11.84, 11.88, 11.92, 11.96, 12.0, 12.02, 12.04]. The historical Relative Humidity data for the past 105 minutes is: [91.24, 91.12, 91.0, 90.76, 90.51, 90.27, 90.02, 89.78, 89.66, 89.54, 89.42, 89.3, 89.18, 89.06, 88.94, 88.83, 88.71, 88.59, 88.35, 88.12, 87.88, 87.65, 87.41, 87.29, 87.18, 87.06, 86.95, 86.83, 88.29, 89.75, 91.22, 92.68, 94.14, 94.02, 93.89, 93.77, 93.64, 93.52, 93.4, 93.27, 93.15, 93.02, 92.9, 92.65, 92.41, 92.16, 91.92, 91.67, 91.55, 91.43, 91.3, 91.18, 91.06, 90.94, 90.82, 90.7, 90.58, 90.46, 90.34, 90.22, 90.1, 89.98, 89.86, 89.62, 89.39, 89.15, 88.92, 88.68, 88.56, 88.45, 88.33, 88.22, 88.1, 87.98, 87.87, 87.75, 87.64, 87.52, 87.29, 87.06, 86.83, 86.6, 86.37, 86.26, 86.14, 86.03, 85.91, 85.8, 87.24, 88.68, 90.13, 91.57, 93.01, 92.89, 92.77, 92.64, 92.52, 92.4, 92.16, 91.92, 91.68, 91.44, 91.2, 91.08, 90.96]. The historical wind_power data for the past 105 minutes is: [0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 71 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_104.pkl | external_data/ground_truth_data/ground_truth_data_104.pkl | external_data/context/context_104.pkl | external_data/constraint/constraint_104.pkl |
105 | electricity_prediction-load_ramp_rate | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 90 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.004825226360636295 MW for each time step. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 36 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 90 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.004825226360636295 MW for each time step. The historical Relative Humidity data for the past 90 minutes is: [69.65, 69.65, 69.65, 69.65, 69.65, 69.55, 69.46, 69.36, 69.27, 69.17, 69.08, 68.98, 68.89, 68.79, 68.7, 68.61, 68.51, 68.42, 68.32, 68.23, 68.07, 67.9, 67.74, 67.57, 67.41, 67.32, 67.23, 67.13, 67.04, 66.95, 66.86, 66.77, 66.67, 66.58, 66.49, 66.4, 66.31, 66.22, 66.13, 66.04, 65.95, 65.86, 65.77, 65.68, 65.59, 65.5, 65.41, 65.32, 65.23, 65.14, 65.05, 64.96, 64.88, 64.79, 64.7, 64.7, 64.7, 64.7, 64.7, 64.7, 64.61, 64.52, 64.44, 64.35, 64.26, 64.17, 64.08, 64.0, 63.91, 63.82, 63.73, 63.65, 63.56, 63.48, 63.39, 63.38, 63.37, 63.35, 63.34, 63.33, 63.18, 63.02, 62.87, 62.71, 62.56, 62.48, 62.39, 62.31, 62.22, 62.14]. The historical Wind Speed data for the past 90 minutes is: [4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.72, 4.74, 4.76, 4.78, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8]. The historical wind_power data for the past 90 minutes is: [0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.31, 0.32, 0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.32, 0.32, 0.32, 0.31, 0.31, 0.31, 0.3, 0.3, 0.3, 0.3, 0.3, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.29, 0.29, 0.29, 0.29, 0.3, 0.3, 0.3, 0.3, 0.3, 0.31, 0.31, 0.32, 0.32, 0.32, 0.33, 0.33, 0.33, 0.33, 0.33, 0.33, 0.32, 0.32, 0.32]. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 36 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_105.pkl | external_data/ground_truth_data/ground_truth_data_105.pkl | external_data/context/context_105.pkl | external_data/constraint/constraint_105.pkl |
106 | electricity_prediction-load_ramp_rate | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 131 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0005776879999444659 MW for each time step. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 82 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 131 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0005776879999444659 MW for each time step. The historical Temperature data for the past 131 minutes is: [17.94, 17.92, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.88, 17.86, 17.84, 17.82, 17.8, 17.78, 17.76, 17.74, 17.72, 17.7, 17.7, 17.7, 17.7, 17.7, 17.7, 17.68, 17.66, 17.64, 17.62, 17.6, 17.6, 17.6, 17.6, 17.6, 17.6, 17.58, 17.56, 17.54, 17.52, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.48, 17.46, 17.44, 17.42, 17.4, 17.38, 17.36, 17.34, 17.32, 17.3, 17.3, 17.3, 17.3, 17.3, 17.3, 17.28, 17.26, 17.24, 17.22, 17.2, 17.2, 17.2, 17.2, 17.2, 17.2, 17.18, 17.16, 17.14, 17.12, 17.1, 17.1, 17.1, 17.1, 17.1, 17.1, 17.08, 17.06, 17.04, 17.02, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 16.98, 16.96, 16.94, 16.92, 16.9, 16.9, 16.9, 16.9, 16.9, 16.9, 16.88, 16.86, 16.84, 16.82, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.8, 16.78, 16.76, 16.74, 16.72, 16.7, 16.7, 16.7, 16.7, 16.7, 16.7, 16.68, 16.66, 16.64, 16.62, 16.6, 16.6, 16.6, 16.6]. The historical Relative Humidity data for the past 131 minutes is: [88.44, 88.55, 88.66, 88.66, 88.66, 88.66, 88.66, 88.66, 88.39, 88.13, 87.86, 87.6, 87.33, 87.44, 87.55, 87.66, 87.77, 87.88, 87.88, 87.88, 87.88, 87.88, 87.88, 87.99, 88.1, 88.21, 88.32, 88.43, 88.43, 88.43, 88.43, 88.43, 88.43, 88.54, 88.65, 88.77, 88.88, 88.99, 88.99, 88.99, 88.99, 88.99, 88.99, 89.08, 89.18, 89.27, 89.37, 89.46, 89.57, 89.69, 89.8, 89.92, 90.03, 90.03, 90.03, 90.03, 90.03, 90.03, 90.14, 90.25, 90.37, 90.48, 90.59, 90.59, 90.59, 90.59, 90.59, 90.59, 90.39, 90.19, 89.99, 89.79, 89.59, 89.59, 89.59, 89.59, 89.59, 89.59, 89.7, 89.81, 89.93, 90.04, 90.15, 90.15, 90.15, 90.15, 90.15, 90.15, 90.27, 90.38, 90.5, 90.61, 90.73, 90.73, 90.73, 90.73, 90.73, 90.73, 90.84, 90.96, 91.07, 91.19, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.3, 91.42, 91.53, 91.65, 91.76, 91.88, 91.88, 91.88, 91.88, 91.88, 91.88, 92.0, 92.11, 92.23, 92.34, 92.46, 92.21, 91.97, 91.72]. The historical wind_power data for the past 131 minutes is: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.01, 0.01]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 82 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_106.pkl | external_data/ground_truth_data/ground_truth_data_106.pkl | external_data/context/context_106.pkl | external_data/constraint/constraint_106.pkl |
107 | electricity_prediction-load_ramp_rate | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 111 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00036189217013990173 MW for each time step. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 40 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 111 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00036189217013990173 MW for each time step. The historical Temperature data for the past 111 minutes is: [30.2, 30.18, 30.16, 30.14, 30.12, 30.1, 30.1, 30.1, 30.1, 30.1, 30.1, 30.08, 30.06, 30.04, 30.02, 30.0, 29.98, 29.96, 29.94, 29.92, 29.9, 29.88, 29.86, 29.84, 29.82, 29.8, 29.76, 29.72, 29.68, 29.64, 29.6, 29.56, 29.52, 29.48, 29.44, 29.4, 29.36, 29.32, 29.28, 29.24, 29.2, 29.14, 29.08, 29.02, 28.96, 28.9, 28.86, 28.82, 28.78, 28.74, 28.7, 28.66, 28.62, 28.58, 28.54, 28.5, 28.46, 28.42, 28.38, 28.34, 28.3, 28.26, 28.22, 28.18, 28.14, 28.1, 28.04, 27.98, 27.92, 27.86, 27.8, 27.76, 27.72, 27.68, 27.64, 27.6, 27.56, 27.52, 27.48, 27.44, 27.4, 27.36, 27.32, 27.28, 27.24, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2]. The historical Relative Humidity data for the past 111 minutes is: [45.98, 46.03, 46.08, 46.14, 46.19, 46.24, 46.24, 46.24, 46.24, 46.24, 46.24, 46.29, 46.35, 46.4, 46.46, 46.51, 46.56, 46.62, 46.67, 46.73, 46.78, 46.83, 46.88, 46.94, 46.99, 47.04, 47.15, 47.26, 47.37, 47.48, 47.59, 47.7, 47.81, 47.92, 48.03, 48.14, 48.25, 48.36, 48.47, 48.58, 48.69, 48.86, 49.03, 49.2, 49.37, 49.54, 49.66, 49.77, 49.89, 50.0, 50.12, 50.24, 50.35, 50.47, 50.58, 50.7, 52.93, 55.16, 57.38, 59.61, 61.84, 61.98, 62.13, 62.27, 62.42, 62.56, 62.78, 63.0, 63.22, 63.44, 63.66, 63.81, 63.96, 64.11, 64.26, 64.41, 64.56, 64.71, 64.86, 65.01, 65.16, 65.31, 65.47, 65.62, 65.78, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93, 65.93]. The historical wind_power data for the past 111 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 40 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_107.pkl | external_data/ground_truth_data/ground_truth_data_107.pkl | external_data/context/context_107.pkl | external_data/constraint/constraint_107.pkl |
108 | electricity_prediction-load_ramp_rate | I have historical DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity data and the corresponding solar_power data for the past 167 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.003721599381207189 MW for each time step. Think about how DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity influence solar_power. Please give me a forecast for the next 77 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity data and the corresponding solar_power data for the past 167 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.003721599381207189 MW for each time step. The historical DNI data for the past 167 minutes is: [914.2, 914.0, 913.4, 912.8, 912.2, 911.6, 911.0, 910.6, 910.2, 909.8, 909.4, 909.0, 908.6, 908.2, 907.8, 907.4, 907.0, 906.6, 906.2, 905.8, 905.4, 905.0, 904.4, 903.8, 903.2, 902.6, 902.0, 901.0, 900.0, 899.0, 898.0, 897.0, 896.4, 895.8, 895.2, 894.6, 894.0, 893.4, 892.8, 892.2, 891.6, 891.0, 890.4, 889.8, 889.2, 888.6, 888.0, 887.2, 886.4, 885.6, 884.8, 884.0, 883.4, 882.8, 882.2, 881.6, 881.0, 880.2, 879.4, 878.6, 877.8, 877.0, 876.2, 875.4, 874.6, 873.8, 873.0, 872.2, 871.4, 870.6, 869.8, 869.0, 868.2, 867.4, 866.6, 865.8, 865.0, 864.0, 863.0, 862.0, 861.0, 860.0, 859.2, 858.4, 857.6, 856.8, 856.0, 854.8, 853.6, 852.4, 851.2, 850.0, 849.0, 848.0, 847.0, 846.0, 845.0, 844.0, 843.0, 842.0, 841.0, 840.0, 838.8, 837.6, 836.4, 835.2, 834.0, 832.8, 831.6, 830.4, 829.2, 828.0, 826.8, 825.6, 824.4, 823.2, 822.0, 820.6, 819.2, 817.8, 816.4, 815.0, 813.6, 812.2, 810.8, 809.4, 808.0, 806.6, 805.2, 803.8, 802.4, 801.0, 799.6, 798.2, 796.8, 795.4, 794.0, 791.8, 789.6, 787.4, 785.2, 783.0, 781.4, 779.8, 778.2, 776.6, 775.0, 772.6, 770.2, 767.8, 765.4, 763.0, 761.2, 759.4, 757.6, 755.8, 754.0, 752.0, 750.0, 748.0, 746.0, 744.0, 742.0, 740.0, 738.0, 736.0, 734.0]. The historical Temperature data for the past 167 minutes is: [21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.22, 21.24, 21.26, 21.28, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.3, 21.28, 21.26, 21.24, 21.22, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.18, 21.16, 21.14, 21.12, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.1, 21.08, 21.06, 21.04, 21.02, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 20.98, 20.96, 20.94, 20.92, 20.9, 20.88, 20.86, 20.84, 20.82, 20.8, 20.8, 20.8, 20.8, 20.8, 20.8, 20.78, 20.76, 20.74, 20.72, 20.7, 20.68, 20.66, 20.64, 20.62, 20.6, 20.58, 20.56, 20.54, 20.52, 20.5, 20.48, 20.46, 20.44, 20.42, 20.4, 20.4, 20.4, 20.4, 20.4, 20.4, 20.38, 20.36, 20.34, 20.32, 20.3, 20.28, 20.26, 20.24, 20.22, 20.2, 20.18, 20.16, 20.14, 20.12, 20.1]. The historical Solar Zenith Angle data for the past 167 minutes is: [32.67, 32.82, 32.97, 33.12, 33.28, 33.43, 33.58, 33.73, 33.89, 34.04, 34.2, 34.35, 34.51, 34.67, 34.82, 34.98, 35.14, 35.3, 35.46, 35.63, 35.79, 35.95, 36.11, 36.28, 36.44, 36.61, 36.77, 36.94, 37.11, 37.28, 37.45, 37.62, 37.79, 37.96, 38.13, 38.3, 38.47, 38.64, 38.82, 38.99, 39.17, 39.34, 39.52, 39.69, 39.87, 40.04, 40.22, 40.4, 40.58, 40.76, 40.94, 41.12, 41.3, 41.48, 41.66, 41.84, 42.02, 42.2, 42.38, 42.57, 42.75, 42.93, 43.12, 43.3, 43.49, 43.67, 43.86, 44.05, 44.23, 44.42, 44.6, 44.79, 44.98, 45.17, 45.35, 45.54, 45.73, 45.92, 46.11, 46.29, 46.48, 46.67, 46.86, 47.05, 47.25, 47.44, 47.63, 47.82, 48.01, 48.21, 48.4, 48.59, 48.78, 48.97, 49.17, 49.36, 49.55, 49.74, 49.94, 50.13, 50.33, 50.52, 50.72, 50.91, 51.11, 51.3, 51.5, 51.7, 51.89, 52.09, 52.28, 52.48, 52.68, 52.88, 53.07, 53.27, 53.47, 53.67, 53.87, 54.06, 54.26, 54.46, 54.66, 54.86, 55.05, 55.25, 55.45, 55.65, 55.85, 56.04, 56.24, 56.44, 56.64, 56.84, 57.04, 57.24, 57.44, 57.64, 57.84, 58.05, 58.25, 58.45, 58.65, 58.85, 59.05, 59.25, 59.45, 59.65, 59.85, 60.06, 60.26, 60.46, 60.66, 60.86, 61.07, 61.27, 61.47, 61.67, 61.87, 62.08, 62.28, 62.48, 62.68, 62.88, 63.09, 63.29, 63.49]. The historical DHI data for the past 167 minutes is: [116.2, 116.0, 116.0, 116.0, 116.0, 116.0, 116.0, 115.8, 115.6, 115.4, 115.2, 115.0, 115.0, 115.0, 115.0, 115.0, 115.0, 114.8, 114.6, 114.4, 114.2, 114.0, 114.0, 114.0, 114.0, 114.0, 114.0, 114.2, 114.4, 114.6, 114.8, 115.0, 114.8, 114.6, 114.4, 114.2, 114.0, 113.8, 113.6, 113.4, 113.2, 113.0, 113.0, 113.0, 113.0, 113.0, 113.0, 112.8, 112.6, 112.4, 112.2, 112.0, 111.8, 111.6, 111.4, 111.2, 111.0, 111.0, 111.0, 111.0, 111.0, 111.0, 110.8, 110.6, 110.4, 110.2, 110.0, 109.8, 109.6, 109.4, 109.2, 109.0, 108.8, 108.6, 108.4, 108.2, 108.0, 107.8, 107.6, 107.4, 107.2, 107.0, 106.8, 106.6, 106.4, 106.2, 106.0, 106.0, 106.0, 106.0, 106.0, 106.0, 105.8, 105.6, 105.4, 105.2, 105.0, 104.8, 104.6, 104.4, 104.2, 104.0, 103.8, 103.6, 103.4, 103.2, 103.0, 102.8, 102.6, 102.4, 102.2, 102.0, 101.8, 101.6, 101.4, 101.2, 101.0, 100.6, 100.2, 99.8, 99.4, 99.0, 98.8, 98.6, 98.4, 98.2, 98.0, 97.8, 97.6, 97.4, 97.2, 97.0, 96.8, 96.6, 96.4, 96.2, 96.0, 95.6, 95.2, 94.8, 94.4, 94.0, 93.8, 93.6, 93.4, 93.2, 93.0, 93.0, 93.0, 93.0, 93.0, 93.0, 92.6, 92.2, 91.8, 91.4, 91.0, 90.6, 90.2, 89.8, 89.4, 89.0, 88.8, 88.6, 88.4, 88.2, 88.0]. The historical Relative Humidity data for the past 167 minutes is: [47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.16, 47.1, 47.04, 46.99, 46.93, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 46.87, 47.03, 47.19, 47.35, 47.51, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.67, 47.66, 47.65, 47.64, 47.63, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.62, 47.68, 47.74, 47.79, 47.85, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.91, 47.97, 48.03, 48.09, 48.15, 48.21, 48.21, 48.21, 48.21, 48.21, 48.21, 48.36, 48.51, 48.66, 48.81, 48.96, 49.02, 49.08, 49.14, 49.2, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.26, 49.32, 49.38, 49.44, 49.5, 49.56, 49.62, 49.68, 49.75, 49.81, 49.87, 49.87, 49.87, 49.87, 49.87, 49.87, 49.93, 49.99, 50.05, 50.11, 50.17, 50.23, 50.29, 50.36, 50.42, 50.48, 50.54, 50.6, 50.67, 50.73, 50.79, 50.85, 50.92, 50.98, 51.05, 51.11, 51.4, 51.68, 51.97, 52.25, 52.54, 52.6, 52.67, 52.73, 52.8, 52.86, 52.93, 52.99, 53.06, 53.12, 53.19, 53.26, 53.32, 53.39, 53.45, 53.52]. The historical solar_power data for the past 167 minutes is: [0.76, 0.76, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.73, 0.73, 0.73, 0.73, 0.73, 0.73, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.71, 0.71, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, 0.7, 0.7, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.68, 0.68, 0.68, 0.68, 0.68, 0.67, 0.67, 0.67, 0.67, 0.66, 0.66, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65, 0.65, 0.65, 0.64, 0.64, 0.64, 0.64, 0.63, 0.63, 0.63, 0.63, 0.63, 0.62, 0.62, 0.62, 0.62, 0.61, 0.61, 0.61, 0.61, 0.61, 0.6, 0.6, 0.6, 0.6, 0.59, 0.59, 0.59, 0.59, 0.58, 0.58, 0.58, 0.58, 0.57, 0.57, 0.57, 0.57, 0.56, 0.56, 0.56, 0.56, 0.55, 0.55, 0.55, 0.54, 0.54, 0.54, 0.54, 0.53, 0.53, 0.53, 0.52, 0.52, 0.52, 0.51, 0.51, 0.51, 0.51, 0.5, 0.5, 0.5, 0.49, 0.49, 0.49, 0.48, 0.48, 0.48, 0.47, 0.47, 0.47, 0.47, 0.46, 0.46, 0.46, 0.45, 0.45, 0.45, 0.44, 0.44, 0.44, 0.43, 0.43, 0.42, 0.42, 0.42, 0.42, 0.41, 0.41, 0.41, 0.4, 0.4, 0.39, 0.39, 0.39, 0.38, 0.38, 0.38, 0.37, 0.37, 0.37, 0.36, 0.36, 0.36, 0.35, 0.35, 0.35, 0.34, 0.34]. Think about how DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity influence solar_power. Please give me a forecast for the next 77 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DNI, Temperature, Solar Zenith Angle, DHI, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_108.pkl | external_data/ground_truth_data/ground_truth_data_108.pkl | external_data/context/context_108.pkl | external_data/constraint/constraint_108.pkl |
109 | electricity_prediction-load_ramp_rate | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 181 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00022140178138869477 MW for each time step. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 43 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 181 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00022140178138869477 MW for each time step. The historical Temperature data for the past 181 minutes is: [19.68, 19.66, 19.64, 19.62, 19.6, 19.58, 19.56, 19.54, 19.52, 19.5, 19.5, 19.5, 19.5, 19.5, 19.5, 19.48, 19.46, 19.44, 19.42, 19.4, 19.4, 19.4, 19.4, 19.4, 19.4, 19.38, 19.36, 19.34, 19.32, 19.3, 19.3, 19.3, 19.3, 19.3, 19.3, 19.28, 19.26, 19.24, 19.22, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.18, 19.16, 19.14, 19.12, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.08, 19.06, 19.04, 19.02, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 18.98, 18.96, 18.94, 18.92, 18.9, 18.9, 18.9, 18.9, 18.9, 18.9, 18.88, 18.86, 18.84, 18.82, 18.8, 18.8, 18.8, 18.8, 18.8, 18.8, 18.78, 18.76, 18.74, 18.72, 18.7, 18.7, 18.7, 18.7, 18.7, 18.7, 18.68, 18.66, 18.64, 18.62, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.58, 18.56, 18.54, 18.52, 18.5, 18.5, 18.5, 18.5, 18.5, 18.5, 18.48, 18.46, 18.44, 18.42, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.38, 18.36, 18.34, 18.32, 18.3, 18.3, 18.3, 18.3, 18.3, 18.3, 18.28, 18.26, 18.24, 18.22, 18.2, 18.2, 18.2, 18.2, 18.2, 18.2, 18.18, 18.16, 18.14, 18.12, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.08, 18.06, 18.04, 18.02, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 17.98, 17.96, 17.94, 17.92, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.88]. The historical Relative Humidity data for the past 181 minutes is: [90.36, 90.47, 90.59, 90.7, 90.81, 90.92, 91.03, 91.15, 91.26, 91.37, 91.37, 91.37, 91.37, 91.37, 91.37, 91.48, 91.6, 91.71, 91.83, 91.94, 91.54, 91.14, 90.73, 90.33, 89.93, 90.04, 90.15, 90.27, 90.38, 90.49, 90.49, 90.49, 90.49, 90.49, 90.49, 90.6, 90.71, 90.83, 90.94, 91.05, 91.05, 91.05, 91.05, 91.05, 91.05, 91.16, 91.28, 91.39, 91.51, 91.62, 91.62, 91.62, 91.62, 91.62, 91.62, 91.73, 91.85, 91.96, 92.08, 92.19, 92.19, 92.19, 92.19, 92.19, 92.19, 92.31, 92.42, 92.54, 92.65, 92.77, 92.77, 92.77, 92.77, 92.77, 92.77, 92.88, 93.0, 93.11, 93.23, 93.34, 93.0, 92.67, 92.33, 92.0, 91.66, 91.77, 91.89, 92.0, 92.12, 92.23, 92.23, 92.23, 92.23, 92.23, 92.23, 92.35, 92.46, 92.58, 92.69, 92.81, 92.81, 92.81, 92.81, 92.81, 92.81, 92.93, 93.04, 93.16, 93.27, 93.39, 93.39, 93.39, 93.39, 93.39, 93.39, 93.51, 93.62, 93.74, 93.85, 93.97, 93.97, 93.97, 93.97, 93.97, 93.97, 94.09, 94.21, 94.32, 94.44, 94.56, 94.56, 94.56, 94.56, 94.56, 94.56, 94.68, 94.8, 94.91, 95.03, 95.15, 94.75, 94.36, 93.96, 93.57, 93.17, 93.29, 93.4, 93.52, 93.63, 93.75, 93.75, 93.75, 93.75, 93.75, 93.75, 93.87, 93.99, 94.1, 94.22, 94.34, 94.34, 94.34, 94.34, 94.34, 94.34, 94.46, 94.58, 94.69, 94.81, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 94.93, 95.05]. The historical wind_power data for the past 181 minutes is: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 43 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_109.pkl | external_data/ground_truth_data/ground_truth_data_109.pkl | external_data/context/context_109.pkl | external_data/constraint/constraint_109.pkl |
110 | electricity_prediction-load_ramp_rate | I have historical GHI, DNI, DHI, Temperature, Dew Point data and the corresponding solar_power data for the past 154 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.013715270295049145 MW for each time step. Think about how GHI, DNI, DHI, Temperature, Dew Point influence solar_power. Please give me a forecast for the next 75 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of GHI, DNI, DHI, Temperature, Dew Point are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical GHI, DNI, DHI, Temperature, Dew Point data and the corresponding solar_power data for the past 154 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.013715270295049145 MW for each time step. The historical GHI data for the past 154 minutes is: [681.6, 684.0, 683.6, 683.2, 682.8, 682.4, 682.0, 681.0, 680.0, 679.0, 678.0, 677.0, 678.4, 679.8, 681.2, 682.6, 684.0, 686.2, 688.4, 690.6, 692.8, 695.0, 691.6, 688.2, 684.8, 681.4, 678.0, 682.0, 686.0, 690.0, 694.0, 698.0, 695.0, 692.0, 689.0, 686.0, 683.0, 682.4, 681.8, 681.2, 680.6, 680.0, 679.2, 678.4, 677.6, 676.8, 676.0, 673.0, 670.0, 667.0, 664.0, 661.0, 664.6, 668.2, 671.8, 675.4, 679.0, 679.0, 679.0, 679.0, 679.0, 679.0, 680.8, 682.6, 684.4, 686.2, 688.0, 686.2, 684.4, 682.6, 680.8, 679.0, 676.2, 673.4, 670.6, 667.8, 665.0, 660.2, 655.4, 650.6, 645.8, 641.0, 646.6, 652.2, 657.8, 663.4, 669.0, 665.2, 661.4, 657.6, 653.8, 650.0, 651.4, 652.8, 654.2, 655.6, 657.0, 655.6, 654.2, 652.8, 651.4, 650.0, 653.0, 656.0, 659.0, 662.0, 665.0, 659.0, 653.0, 647.0, 641.0, 635.0, 636.4, 637.8, 639.2, 640.6, 642.0, 636.6, 631.2, 625.8, 620.4, 615.0, 609.2, 603.4, 597.6, 591.8, 586.0, 588.8, 591.6, 594.4, 597.2, 600.0, 597.4, 594.8, 592.2, 589.6, 587.0, 583.2, 579.4, 575.6, 571.8, 568.0, 565.8, 563.6, 561.4, 559.2, 557.0, 555.8, 554.6, 553.4, 552.2, 551.0, 547.8, 544.6]. The historical DNI data for the past 154 minutes is: [568.8, 573.0, 571.0, 569.0, 567.0, 565.0, 563.0, 559.6, 556.2, 552.8, 549.4, 546.0, 548.8, 551.6, 554.4, 557.2, 560.0, 564.4, 568.8, 573.2, 577.6, 582.0, 574.0, 566.0, 558.0, 550.0, 542.0, 551.0, 560.0, 569.0, 578.0, 587.0, 580.4, 573.8, 567.2, 560.6, 554.0, 552.6, 551.2, 549.8, 548.4, 547.0, 545.6, 544.2, 542.8, 541.4, 540.0, 533.4, 526.8, 520.2, 513.6, 507.0, 516.2, 525.4, 534.6, 543.8, 553.0, 554.2, 555.4, 556.6, 557.8, 559.0, 564.6, 570.2, 575.8, 581.4, 587.0, 584.2, 581.4, 578.6, 575.8, 573.0, 568.0, 563.0, 558.0, 553.0, 548.0, 537.8, 527.6, 517.4, 507.2, 497.0, 513.0, 529.0, 545.0, 561.0, 577.0, 570.0, 563.0, 556.0, 549.0, 542.0, 547.8, 553.6, 559.4, 565.2, 571.0, 570.2, 569.4, 568.6, 567.8, 567.0, 578.0, 589.0, 600.0, 611.0, 622.0, 609.0, 596.0, 583.0, 570.0, 557.0, 564.4, 571.8, 579.2, 586.6, 594.0, 582.8, 571.6, 560.4, 549.2, 538.0, 523.6, 509.2, 494.8, 480.4, 466.0, 479.8, 493.6, 507.4, 521.2, 535.0, 531.4, 527.8, 524.2, 520.6, 517.0, 508.4, 499.8, 491.2, 482.6, 474.0, 471.2, 468.4, 465.6, 462.8, 460.0, 461.6, 463.2, 464.8, 466.4, 468.0, 461.0, 454.0]. The historical DHI data for the past 154 minutes is: [255.2, 254.0, 254.8, 255.6, 256.4, 257.2, 258.0, 259.4, 260.8, 262.2, 263.6, 265.0, 264.2, 263.4, 262.6, 261.8, 261.0, 259.6, 258.2, 256.8, 255.4, 254.0, 256.8, 259.6, 262.4, 265.2, 268.0, 265.0, 262.0, 259.0, 256.0, 253.0, 255.2, 257.4, 259.6, 261.8, 264.0, 264.4, 264.8, 265.2, 265.6, 266.0, 266.4, 266.8, 267.2, 267.6, 268.0, 270.0, 272.0, 274.0, 276.0, 278.0, 274.8, 271.6, 268.4, 265.2, 262.0, 261.4, 260.8, 260.2, 259.6, 259.0, 256.8, 254.6, 252.4, 250.2, 248.0, 248.8, 249.6, 250.4, 251.2, 252.0, 253.2, 254.4, 255.6, 256.8, 258.0, 261.2, 264.4, 267.6, 270.8, 274.0, 268.2, 262.4, 256.6, 250.8, 245.0, 246.8, 248.6, 250.4, 252.2, 254.0, 251.6, 249.2, 246.8, 244.4, 242.0, 241.8, 241.6, 241.4, 241.2, 241.0, 236.6, 232.2, 227.8, 223.4, 219.0, 223.0, 227.0, 231.0, 235.0, 239.0, 235.8, 232.6, 229.4, 226.2, 223.0, 226.2, 229.4, 232.6, 235.8, 239.0, 244.0, 249.0, 254.0, 259.0, 264.0, 257.8, 251.6, 245.4, 239.2, 233.0, 233.6, 234.2, 234.8, 235.4, 236.0, 238.8, 241.6, 244.4, 247.2, 250.0, 250.4, 250.8, 251.2, 251.6, 252.0, 250.6, 249.2, 247.8, 246.4, 245.0, 247.0, 249.0]. The historical Temperature data for the past 154 minutes is: [10.18, 10.2, 10.22, 10.24, 10.26, 10.28, 10.3, 10.32, 10.34, 10.36, 10.38, 10.4, 10.42, 10.44, 10.46, 10.48, 10.5, 10.52, 10.54, 10.56, 10.58, 10.6, 10.6, 10.6, 10.6, 10.6, 10.6, 10.62, 10.64, 10.66, 10.68, 10.7, 10.7, 10.7, 10.7, 10.7, 10.7, 10.72, 10.74, 10.76, 10.78, 10.8, 10.82, 10.84, 10.86, 10.88, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.92, 10.94, 10.96, 10.98, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.02, 11.04, 11.06, 11.08, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.12, 11.14, 11.16, 11.18, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.22, 11.24, 11.26, 11.28, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.32, 11.34, 11.36, 11.38, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.38, 11.36, 11.34, 11.32, 11.3, 11.3, 11.3]. The historical Dew Point data for the past 154 minutes is: [-0.5, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.4, -0.3, -0.2, -0.1, -0.0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.08, 0.06, 0.04, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.08, 0.12, 0.16, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]. The historical solar_power data for the past 154 minutes is: [0.65, 0.66, 0.66, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.66, 0.66, 0.66, 0.66, 0.67, 0.66, 0.66, 0.65, 0.65, 0.65, 0.65, 0.66, 0.66, 0.66, 0.67, 0.67, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.64, 0.64, 0.64, 0.63, 0.63, 0.63, 0.63, 0.64, 0.64, 0.64, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.65, 0.66, 0.66, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65, 0.65, 0.64, 0.64, 0.64, 0.64, 0.63, 0.62, 0.62, 0.61, 0.61, 0.62, 0.62, 0.63, 0.64, 0.64, 0.64, 0.63, 0.63, 0.63, 0.62, 0.62, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63, 0.63, 0.62, 0.63, 0.63, 0.64, 0.64, 0.65, 0.64, 0.63, 0.62, 0.62, 0.61, 0.61, 0.61, 0.62, 0.62, 0.62, 0.61, 0.61, 0.6, 0.6, 0.59, 0.58, 0.58, 0.57, 0.56, 0.56, 0.56, 0.57, 0.57, 0.57, 0.58, 0.58, 0.57, 0.57, 0.57, 0.56, 0.56, 0.56, 0.55, 0.55, 0.54, 0.54, 0.54, 0.54, 0.53, 0.53, 0.53, 0.53, 0.53, 0.53, 0.53, 0.52, 0.52]. Think about how GHI, DNI, DHI, Temperature, Dew Point influence solar_power. Please give me a forecast for the next 75 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of GHI, DNI, DHI, Temperature, Dew Point are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_110.pkl | external_data/ground_truth_data/ground_truth_data_110.pkl | external_data/context/context_110.pkl | external_data/constraint/constraint_110.pkl |
111 | electricity_prediction-load_ramp_rate | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 98 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00032704362422570276 MW for each time step. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 11 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 98 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00032704362422570276 MW for each time step. The historical Relative Humidity data for the past 98 minutes is: [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]. The historical Temperature data for the past 98 minutes is: [16.3, 16.3, 16.3, 16.28, 16.26, 16.24, 16.22, 16.2, 16.2, 16.2, 16.2, 16.2, 16.2, 16.18, 16.16, 16.14, 16.12, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.1, 16.08, 16.06, 16.04, 16.02, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 15.98, 15.96, 15.94, 15.92, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.9, 15.88, 15.86, 15.84, 15.82, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.78, 15.76, 15.74, 15.72, 15.7, 15.7, 15.7, 15.7, 15.7, 15.7, 15.68, 15.66, 15.64, 15.62, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.6, 15.58, 15.56, 15.54, 15.52, 15.5]. The historical wind_power data for the past 98 minutes is: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 11 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_111.pkl | external_data/ground_truth_data/ground_truth_data_111.pkl | external_data/context/context_111.pkl | external_data/constraint/constraint_111.pkl |
112 | electricity_prediction-load_ramp_rate | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 137 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0007454246538536302 MW for each time step. Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 46 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 137 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0007454246538536302 MW for each time step. The historical Temperature data for the past 137 minutes is: [29.66, 29.68, 29.7, 29.72, 29.74, 29.76, 29.78, 29.8, 29.82, 29.84, 29.86, 29.88, 29.9, 29.92, 29.94, 29.96, 29.98, 30.0, 30.02, 30.04, 30.06, 30.08, 30.1, 30.12, 30.14, 30.16, 30.18, 30.2, 30.2, 30.2, 30.2, 30.2, 30.2, 30.22, 30.24, 30.26, 30.28, 30.3, 30.3, 30.3, 30.3, 30.3, 30.3, 30.32, 30.34, 30.36, 30.38, 30.4, 30.4, 30.4, 30.4, 30.4, 30.4, 30.42, 30.44, 30.46, 30.48, 30.5, 30.52, 30.54, 30.56, 30.58, 30.6, 30.6, 30.6, 30.6, 30.6, 30.6, 30.62, 30.64, 30.66, 30.68, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.72, 30.74, 30.76, 30.78, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.8, 30.82, 30.84, 30.86, 30.88, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.9, 30.92, 30.94, 30.96, 30.98, 31.0, 31.0, 31.0, 31.0, 31.0]. The historical Wind Speed data for the past 137 minutes is: [1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.78, 1.76, 1.74, 1.72, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.68, 1.66, 1.64, 1.62, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6, 1.6]. The historical wind_power data for the past 137 minutes is: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]. Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 46 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_112.pkl | external_data/ground_truth_data/ground_truth_data_112.pkl | external_data/context/context_112.pkl | external_data/constraint/constraint_112.pkl |
113 | electricity_prediction-load_ramp_rate | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 106 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0008125428427767396 MW for each time step. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 82 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 106 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0008125428427767396 MW for each time step. The historical Relative Humidity data for the past 106 minutes is: [70.81, 70.48, 70.16, 69.83, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.51, 69.59, 69.67, 69.76, 69.84, 69.92, 69.92, 69.92, 69.92, 69.92, 69.92, 69.93, 69.95, 69.96, 69.98, 69.99, 70.07, 70.16, 70.24, 70.33, 70.41, 70.41, 70.41, 70.41, 70.41, 70.41, 70.49, 70.58, 70.66, 70.75, 70.83, 70.83, 70.83, 70.83, 70.83, 70.83, 70.91, 71.0, 71.08, 71.17, 71.25, 71.25, 71.25, 71.25, 71.25, 71.25, 70.92, 70.6, 70.27, 69.95, 69.62, 69.62, 69.62, 69.62, 69.62, 69.62, 69.7, 69.79, 69.87, 69.96, 70.04, 70.04, 70.04, 70.04, 70.04, 70.04, 70.12, 70.21, 70.29, 70.38, 70.46, 70.46, 70.46, 70.46, 70.46, 70.46, 70.54, 70.63, 70.71, 70.8, 70.88, 70.97, 71.05, 71.14, 71.22, 71.31, 71.31, 71.31, 71.31, 71.31, 71.31, 71.4]. The historical Temperature data for the past 106 minutes is: [24.98, 24.96, 24.94, 24.92, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.88, 24.86, 24.84, 24.82, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.78, 24.76, 24.74, 24.72, 24.7, 24.7, 24.7, 24.7, 24.7, 24.7, 24.68, 24.66, 24.64, 24.62, 24.6, 24.6, 24.6, 24.6, 24.6, 24.6, 24.58, 24.56, 24.54, 24.52, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.48, 24.46, 24.44, 24.42, 24.4, 24.4, 24.4, 24.4, 24.4, 24.4, 24.38, 24.36, 24.34, 24.32, 24.3, 24.3, 24.3, 24.3, 24.3, 24.3, 24.28, 24.26, 24.24, 24.22, 24.2, 24.2, 24.2, 24.2, 24.2, 24.2, 24.18, 24.16, 24.14, 24.12, 24.1, 24.08, 24.06, 24.04, 24.02, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 23.98]. The historical wind_power data for the past 106 minutes is: [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 82 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_113.pkl | external_data/ground_truth_data/ground_truth_data_113.pkl | external_data/context/context_113.pkl | external_data/constraint/constraint_113.pkl |
114 | electricity_prediction-load_ramp_rate | I have historical Dew Point, Wind Speed, Temperature data and the corresponding load_power data for the past 88 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00041519833646175487 MW for each time step. Think about how Dew Point, Wind Speed, Temperature influence load_power. Please give me a forecast for the next 86 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Wind Speed, Temperature data and the corresponding load_power data for the past 88 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.00041519833646175487 MW for each time step. The historical Dew Point data for the past 88 minutes is: [9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.64, 9.58, 9.52, 9.46, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4]. The historical Wind Speed data for the past 88 minutes is: [2.3, 2.28, 2.26, 2.24, 2.22, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.18, 2.16, 2.14, 2.12, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.08, 2.06, 2.04, 2.02, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.98, 1.96, 1.94, 1.92, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.88, 1.86, 1.84, 1.82, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.8, 1.78, 1.76, 1.74, 1.72, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7, 1.7]. The historical Temperature data for the past 88 minutes is: [13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.6, 13.58, 13.56, 13.54, 13.52, 13.5, 13.5, 13.5, 13.5, 13.5, 13.5, 13.48, 13.46, 13.44, 13.42, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.4, 13.38, 13.36, 13.34, 13.32, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.28, 13.26, 13.24, 13.22, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2]. The historical load_power data for the past 88 minutes is: [0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.84, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.83, 0.82, 0.82, 0.82, 0.83, 0.83]. Think about how Dew Point, Wind Speed, Temperature influence load_power. Please give me a forecast for the next 86 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_114.pkl | external_data/ground_truth_data/ground_truth_data_114.pkl | external_data/context/context_114.pkl | external_data/constraint/constraint_114.pkl |
115 | electricity_prediction-load_ramp_rate | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 197 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.013284605238025082 MW for each time step. Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 46 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 197 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.013284605238025082 MW for each time step. The historical Temperature data for the past 197 minutes is: [6.68, 6.66, 6.64, 6.62, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.58, 6.56, 6.54, 6.52, 6.5, 6.5, 6.5, 6.5, 6.5, 6.5, 6.48, 6.46, 6.44, 6.42, 6.4, 6.38, 6.36, 6.34, 6.32, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.28, 6.26, 6.24, 6.22, 6.2, 6.18, 6.16, 6.14, 6.12, 6.1, 6.08, 6.06, 6.04, 6.02, 6.0, 5.98, 5.96, 5.94, 5.92, 5.9, 5.88, 5.86, 5.84, 5.82, 5.8, 5.8, 5.8, 5.8, 5.8, 5.8, 5.78, 5.76, 5.74, 5.72, 5.7, 5.68, 5.66, 5.64, 5.62, 5.6, 5.58, 5.56, 5.54, 5.52, 5.5, 5.48, 5.46, 5.44, 5.42, 5.4, 5.38, 5.36, 5.34, 5.32, 5.3, 5.28, 5.26, 5.24, 5.22, 5.2, 5.18, 5.16, 5.14, 5.12, 5.1, 5.08, 5.06, 5.04, 5.02, 5.0, 4.98, 4.96, 4.94, 4.92, 4.9, 4.88, 4.86, 4.84, 4.82, 4.8, 4.78, 4.76, 4.74, 4.72, 4.7, 4.68, 4.66, 4.64, 4.62, 4.6, 4.58, 4.56, 4.54, 4.52, 4.5, 4.48, 4.46, 4.44, 4.42, 4.4, 4.38, 4.36, 4.34, 4.32, 4.3, 4.28, 4.26, 4.24, 4.22, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2]. The historical Wind Speed data for the past 197 minutes is: [3.9, 3.9, 3.9, 3.9, 3.9, 3.88, 3.86, 3.84, 3.82, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.78, 3.76, 3.74, 3.72, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.68, 3.66, 3.64, 3.62, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.58, 3.56, 3.54, 3.52, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.48, 3.46, 3.44, 3.42, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.4, 3.38, 3.36, 3.34, 3.32, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.28, 3.26, 3.24, 3.22, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.2, 3.18, 3.16, 3.14, 3.12, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1, 3.08, 3.06, 3.04, 3.02, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.98, 2.96, 2.94, 2.92, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9]. The historical wind_power data for the past 197 minutes is: [0.18, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.17, 0.17, 0.17, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.18, 0.17, 0.17, 0.16, 0.16, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.11, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07]. Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 46 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_115.pkl | external_data/ground_truth_data/ground_truth_data_115.pkl | external_data/context/context_115.pkl | external_data/constraint/constraint_115.pkl |
116 | electricity_prediction-load_ramp_rate | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 73 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0013440811578249016 MW for each time step. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 23 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Temperature data and the corresponding wind_power data for the past 73 minutes. I must monitor the load ramp rate to ensure it does not exceed 0.0013440811578249016 MW for each time step. The historical Wind Speed data for the past 73 minutes is: [2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.38, 2.36, 2.34, 2.32, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.28, 2.26, 2.24, 2.22, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2, 2.2]. The historical Temperature data for the past 73 minutes is: [23.4, 23.4, 23.4, 23.4, 23.38, 23.36, 23.34, 23.32, 23.3, 23.26, 23.22, 23.18, 23.14, 23.1, 23.08, 23.06, 23.04, 23.02, 23.0, 22.98, 22.96, 22.94, 22.92, 22.9, 22.88, 22.86, 22.84, 22.82, 22.8, 22.78, 22.76, 22.74, 22.72, 22.7, 22.66, 22.62, 22.58, 22.54, 22.5, 22.48, 22.46, 22.44, 22.42, 22.4, 22.38, 22.36, 22.34, 22.32, 22.3, 22.28, 22.26, 22.24, 22.22, 22.2, 22.16, 22.12, 22.08, 22.04, 22.0, 21.98, 21.96, 21.94, 21.92, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9, 21.9]. The historical wind_power data for the past 73 minutes is: [0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]. Think about how Wind Speed, Temperature influence wind_power. Please give me a forecast for the next 23 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_116.pkl | external_data/ground_truth_data/ground_truth_data_116.pkl | external_data/context/context_116.pkl | external_data/constraint/constraint_116.pkl |
117 | electricity_prediction-load_variability_limit | I have historical Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature data and the corresponding solar_power data for the past 133 minutes. I need to manage the load variability so that it does not exceed 0.13126353491101753 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature influence solar_power. Please give me a forecast for the next 55 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature data and the corresponding solar_power data for the past 133 minutes. I need to manage the load variability so that it does not exceed 0.13126353491101753 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Relative Humidity data for the past 133 minutes is: [87.13, 87.01, 86.88, 86.76, 86.63, 86.51, 86.51, 86.51, 86.51, 86.51, 86.51, 87.24, 87.97, 88.69, 89.42, 90.15, 90.15, 90.15, 90.15, 90.15, 90.15, 90.02, 89.89, 89.76, 89.63, 89.5, 89.5, 89.5, 89.5, 89.5, 89.5, 89.37, 89.24, 89.12, 88.99, 88.86, 88.86, 88.86, 88.86, 88.86, 88.86, 88.73, 88.61, 88.48, 88.36, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.23, 88.1, 87.98, 87.85, 87.73, 87.6, 87.58, 87.56, 87.55, 87.53, 87.51, 87.94, 88.38, 88.81, 89.25, 89.68, 89.55, 89.42, 89.3, 89.17, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 89.04, 88.91, 88.78, 88.66, 88.53, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.4, 88.54, 88.67]. The historical GHI data for the past 133 minutes is: [313.0, 304.8, 296.6, 288.4, 280.2, 272.0, 269.6, 267.2, 264.8, 262.4, 260.0, 262.6, 265.2, 267.8, 270.4, 273.0, 255.4, 237.8, 220.2, 202.6, 185.0, 184.6, 184.2, 183.8, 183.4, 183.0, 179.2, 175.4, 171.6, 167.8, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 160.2, 161.4, 162.6, 163.8, 165.0, 165.0, 165.0, 165.0, 165.0, 165.0, 166.2, 167.4, 168.6, 169.8, 171.0, 170.6, 170.2, 169.8, 169.4, 169.0, 163.0, 157.0, 151.0, 145.0, 139.0, 144.0, 149.0, 154.0, 159.0, 164.0, 167.4, 170.8, 174.2, 177.6, 181.0, 186.8, 192.6, 198.4, 204.2, 210.0, 212.6, 215.2, 217.8, 220.4, 223.0, 245.0, 267.0, 289.0, 311.0, 333.0, 332.8, 332.6, 332.4, 332.2, 332.0, 337.2, 342.4, 347.6, 352.8, 358.0, 359.6, 361.2, 362.8, 364.4, 366.0, 359.2, 352.4, 345.6, 338.8, 332.0, 325.4, 318.8, 312.2, 305.6, 299.0, 297.0, 295.0, 293.0, 291.0, 289.0, 297.6, 306.2, 314.8, 323.4, 332.0, 300.2, 268.4, 236.6, 204.8, 173.0, 187.8, 202.6, 217.4, 232.2, 247.0, 220.6, 194.2]. The historical Solar Zenith Angle data for the past 133 minutes is: [58.02, 57.98, 57.94, 57.9, 57.86, 57.82, 57.78, 57.75, 57.71, 57.68, 57.64, 57.61, 57.58, 57.54, 57.51, 57.48, 57.45, 57.42, 57.4, 57.37, 57.34, 57.32, 57.29, 57.27, 57.24, 57.22, 57.2, 57.18, 57.17, 57.15, 57.13, 57.12, 57.1, 57.09, 57.07, 57.06, 57.05, 57.04, 57.03, 57.02, 57.01, 57.01, 57.0, 57.0, 56.99, 56.99, 56.99, 56.99, 56.98, 56.98, 56.98, 56.98, 56.99, 56.99, 57.0, 57.0, 57.01, 57.02, 57.02, 57.03, 57.04, 57.05, 57.07, 57.08, 57.1, 57.11, 57.13, 57.14, 57.16, 57.17, 57.19, 57.21, 57.23, 57.26, 57.28, 57.3, 57.33, 57.35, 57.38, 57.4, 57.43, 57.46, 57.49, 57.53, 57.56, 57.59, 57.62, 57.66, 57.69, 57.73, 57.76, 57.8, 57.84, 57.88, 57.92, 57.96, 58.0, 58.04, 58.09, 58.13, 58.17, 58.22, 58.27, 58.31, 58.36, 58.41, 58.46, 58.51, 58.57, 58.62, 58.67, 58.73, 58.78, 58.84, 58.89, 58.95, 59.01, 59.07, 59.13, 59.19, 59.25, 59.31, 59.38, 59.44, 59.51, 59.57, 59.64, 59.71, 59.77, 59.84, 59.91, 59.98, 60.05]. The historical DHI data for the past 133 minutes is: [240.0, 238.6, 237.2, 235.8, 234.4, 233.0, 232.2, 231.4, 230.6, 229.8, 229.0, 230.2, 231.4, 232.6, 233.8, 235.0, 224.2, 213.4, 202.6, 191.8, 181.0, 180.6, 180.2, 179.8, 179.4, 179.0, 176.0, 173.0, 170.0, 167.0, 164.0, 163.0, 162.0, 161.0, 160.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 164.2, 164.4, 164.6, 164.8, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 169.6, 169.2, 168.8, 168.4, 168.0, 162.2, 156.4, 150.6, 144.8, 139.0, 144.0, 149.0, 154.0, 159.0, 164.0, 166.8, 169.6, 172.4, 175.2, 178.0, 182.4, 186.8, 191.2, 195.6, 200.0, 201.8, 203.6, 205.4, 207.2, 209.0, 216.0, 223.0, 230.0, 237.0, 244.0, 243.6, 243.2, 242.8, 242.4, 242.0, 240.8, 239.6, 238.4, 237.2, 236.0, 234.8, 233.6, 232.4, 231.2, 230.0, 231.2, 232.4, 233.6, 234.8, 236.0, 235.6, 235.2, 234.8, 234.4, 234.0, 233.4, 232.8, 232.2, 231.6, 231.0, 230.2, 229.4, 228.6, 227.8, 227.0, 215.6, 204.2, 192.8, 181.4, 170.0, 178.8, 187.6, 196.4, 205.2, 214.0, 194.2, 174.4]. The historical Temperature data for the past 133 minutes is: [0.7, 0.72, 0.74, 0.76, 0.78, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.82, 0.84, 0.86, 0.88, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.92, 0.94, 0.96, 0.98, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.02, 1.04, 1.06, 1.08, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.12, 1.14, 1.16, 1.18, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.22, 1.24, 1.26, 1.28, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.32, 1.34, 1.36, 1.38, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.42, 1.44, 1.46, 1.48, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5]. The historical solar_power data for the past 133 minutes is: [0.32, 0.3, 0.29, 0.28, 0.27, 0.26, 0.25, 0.25, 0.25, 0.24, 0.24, 0.24, 0.25, 0.25, 0.25, 0.26, 0.24, 0.21, 0.19, 0.17, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.12, 0.12, 0.11, 0.12, 0.12, 0.12, 0.13, 0.13, 0.14, 0.14, 0.14, 0.15, 0.15, 0.16, 0.16, 0.17, 0.17, 0.18, 0.18, 0.18, 0.19, 0.19, 0.19, 0.22, 0.25, 0.28, 0.31, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.35, 0.35, 0.36, 0.37, 0.38, 0.38, 0.38, 0.39, 0.39, 0.39, 0.38, 0.37, 0.36, 0.35, 0.34, 0.33, 0.32, 0.31, 0.31, 0.3, 0.29, 0.29, 0.29, 0.28, 0.28, 0.29, 0.3, 0.32, 0.33, 0.34, 0.3, 0.26, 0.22, 0.18, 0.14, 0.16, 0.18, 0.19, 0.21, 0.23, 0.2, 0.17]. Think about how Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature influence solar_power. Please give me a forecast for the next 55 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, GHI, Solar Zenith Angle, DHI, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_117.pkl | external_data/ground_truth_data/ground_truth_data_117.pkl | external_data/context/context_117.pkl | external_data/constraint/constraint_117.pkl |
118 | electricity_prediction-load_variability_limit | I have historical Dew Point, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 140 minutes. I need to manage the load variability so that it does not exceed 0.06594209017349245 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Dew Point, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 87 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 140 minutes. I need to manage the load variability so that it does not exceed 0.06594209017349245 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Dew Point data for the past 140 minutes is: [23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.8, 23.72, 23.64, 23.56, 23.48, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.4, 23.42, 23.44, 23.46, 23.48, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.5, 23.34, 23.18, 23.02, 22.86, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.52, 22.34, 22.16, 21.98, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8, 21.8]. The historical Relative Humidity data for the past 140 minutes is: [93.05, 92.94, 92.94, 92.94, 92.94, 92.94, 92.94, 92.47, 91.99, 91.52, 91.04, 90.57, 90.46, 90.36, 90.25, 90.15, 90.04, 90.04, 90.04, 90.04, 90.04, 90.04, 89.93, 89.82, 89.72, 89.61, 89.5, 89.5, 89.5, 89.5, 89.5, 89.5, 89.4, 89.29, 89.19, 89.08, 88.98, 88.87, 88.77, 88.66, 88.56, 88.45, 88.45, 88.45, 88.45, 88.45, 88.45, 88.35, 88.24, 88.14, 88.03, 87.93, 87.83, 87.72, 87.62, 87.51, 87.41, 87.31, 87.21, 87.1, 87.0, 86.9, 86.9, 86.9, 86.9, 86.9, 86.9, 86.06, 85.22, 84.39, 83.55, 82.71, 82.61, 82.52, 82.42, 82.33, 82.23, 82.23, 82.23, 82.23, 82.23, 82.23, 82.13, 82.03, 81.94, 81.84, 81.74, 81.64, 81.55, 81.45, 81.36, 81.26, 81.17, 81.07, 80.98, 80.88, 80.79, 80.69, 80.6, 80.5, 80.41, 80.31, 80.12, 79.94, 79.75, 79.57, 79.38, 79.29, 79.19, 79.1, 79.0, 78.91, 78.73, 78.54, 78.36, 78.17, 77.99, 77.81, 77.63, 77.45, 77.27, 77.09, 77.0, 76.91, 76.82, 76.73, 76.64, 75.63, 74.62, 73.6, 72.59, 71.58, 71.41, 71.25, 71.08, 70.92, 70.75, 70.67, 70.59, 70.5]. The historical Solar Zenith Angle data for the past 140 minutes is: [71.81, 71.61, 71.41, 71.2, 71.0, 70.79, 70.59, 70.39, 70.19, 69.98, 69.78, 69.58, 69.38, 69.17, 68.97, 68.76, 68.56, 68.36, 68.16, 67.95, 67.75, 67.55, 67.35, 67.14, 66.94, 66.73, 66.53, 66.33, 66.12, 65.92, 65.71, 65.51, 65.31, 65.11, 64.9, 64.7, 64.5, 64.3, 64.09, 63.89, 63.68, 63.48, 63.28, 63.07, 62.87, 62.66, 62.46, 62.26, 62.05, 61.85, 61.64, 61.44, 61.24, 61.03, 60.83, 60.62, 60.42, 60.22, 60.02, 59.81, 59.61, 59.41, 59.21, 59.0, 58.8, 58.59, 58.39, 58.19, 57.98, 57.78, 57.57, 57.37, 57.17, 56.97, 56.76, 56.56, 56.36, 56.16, 55.96, 55.75, 55.55, 55.35, 55.15, 54.95, 54.74, 54.54, 54.34, 54.14, 53.94, 53.73, 53.53, 53.33, 53.13, 52.93, 52.72, 52.52, 52.32, 52.12, 51.92, 51.71, 51.51, 51.31, 51.11, 50.91, 50.71, 50.51, 50.31, 50.11, 49.91, 49.71, 49.51, 49.31, 49.11, 48.91, 48.72, 48.52, 48.32, 48.12, 47.92, 47.72, 47.52, 47.32, 47.12, 46.92, 46.73, 46.53, 46.33, 46.13, 45.94, 45.74, 45.55, 45.35, 45.15, 44.96, 44.76, 44.57, 44.37, 44.17, 43.98, 43.78]. The historical load_power data for the past 140 minutes is: [1.02, 1.02, 1.02, 1.02, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.01, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.02, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.03, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.04, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05, 1.05]. Think about how Dew Point, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 87 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_118.pkl | external_data/ground_truth_data/ground_truth_data_118.pkl | external_data/context/context_118.pkl | external_data/constraint/constraint_118.pkl |
119 | electricity_prediction-load_variability_limit | I have historical DHI, Dew Point, GHI, Relative Humidity, DNI data and the corresponding solar_power data for the past 170 minutes. I need to manage the load variability so that it does not exceed 0.2563967334111119 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how DHI, Dew Point, GHI, Relative Humidity, DNI influence solar_power. Please give me a forecast for the next 83 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, Dew Point, GHI, Relative Humidity, DNI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical DHI, Dew Point, GHI, Relative Humidity, DNI data and the corresponding solar_power data for the past 170 minutes. I need to manage the load variability so that it does not exceed 0.2563967334111119 MW over the complete time period (i.e the maximum change in load over the entire period). The historical DHI data for the past 170 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.6, 5.2, 7.8, 10.4, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.2, 20.4, 21.6, 22.8, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.2, 31.4, 32.6, 33.8, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 50.8, 51.6, 52.4, 53.2, 54.0, 54.8, 55.6, 56.4, 57.2, 58.0, 58.8, 59.6, 60.4, 61.2, 62.0, 62.8, 63.6, 64.4, 65.2, 66.0, 66.6, 67.2, 67.8, 68.4, 69.0, 69.6, 70.2, 70.8, 71.4, 72.0, 72.6, 73.2]. The historical Dew Point data for the past 170 minutes is: [22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.7, 22.76, 22.82, 22.88, 22.94, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.04, 23.08, 23.12, 23.16, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.2, 23.14, 23.08, 23.02, 22.96, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9, 22.9]. The historical GHI data for the past 170 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.8, 5.6, 8.4, 11.2, 14.0, 15.6, 17.2, 18.8, 20.4, 22.0, 24.0, 26.0, 28.0, 30.0, 32.0, 34.2, 36.4, 38.6, 40.8, 43.0, 45.4, 47.8, 50.2, 52.6, 55.0, 57.6, 60.2, 62.8, 65.4, 68.0, 70.6, 73.2, 75.8, 78.4, 81.0, 84.0, 87.0, 90.0, 93.0, 96.0, 99.0, 102.0, 105.0, 108.0, 111.0, 114.0, 117.0, 120.0, 123.0, 126.0, 129.2, 132.4, 135.6, 138.8, 142.0, 145.2, 148.4, 151.6, 154.8, 158.0, 161.4, 164.8, 168.2, 171.6, 175.0, 178.4, 181.8, 185.2, 188.6, 192.0, 195.4, 198.8]. The historical Relative Humidity data for the past 170 minutes is: [75.42, 75.42, 75.42, 75.51, 75.6, 75.68, 75.77, 75.86, 76.13, 76.4, 76.67, 76.94, 77.21, 77.3, 77.39, 77.48, 77.57, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.75, 77.84, 77.93, 78.02, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.11, 78.02, 77.93, 77.84, 77.75, 77.66, 77.66, 77.66, 77.66, 77.66, 77.66, 77.57, 77.48, 77.39, 77.3, 77.21, 77.12, 77.03, 76.94, 76.85, 76.76, 76.77, 76.79, 76.8, 76.82, 76.83, 76.97, 77.11, 77.26, 77.4, 77.54, 77.54, 77.54, 77.54, 77.54, 77.54, 77.45, 77.36, 77.27, 77.18, 77.09, 77.09, 77.09, 77.09, 77.09, 77.09, 77.0, 76.91, 76.82, 76.73, 76.64, 76.55, 76.46, 76.38, 76.29, 76.2, 76.02, 75.85, 75.67, 75.5, 75.32, 75.15, 74.97, 74.8, 74.62, 74.45, 74.28, 74.11, 73.93, 73.76, 73.59, 73.42, 73.25, 73.09, 72.92, 72.75, 72.58, 72.41, 72.25, 72.08, 71.91, 71.75, 71.58, 71.42, 71.25, 71.09, 70.59, 70.09, 69.58, 69.08, 68.58, 68.42, 68.27, 68.11, 67.96, 67.8, 67.65, 67.49, 67.34, 67.18, 67.03, 66.89, 66.75, 66.61, 66.47, 66.33, 66.18, 66.03, 65.88, 65.73, 65.58, 65.43, 65.28, 65.13, 64.98, 64.83, 64.61, 64.39, 64.17, 63.95, 63.73, 63.59, 63.44, 63.3, 63.15, 63.01, 62.8, 62.59]. The historical DNI data for the past 170 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.2, 20.4, 30.6, 40.8, 51.0, 58.2, 65.4, 72.6, 79.8, 87.0, 94.6, 102.2, 109.8, 117.4, 125.0, 133.0, 141.0, 149.0, 157.0, 165.0, 172.8, 180.6, 188.4, 196.2, 204.0, 211.4, 218.8, 226.2, 233.6, 241.0, 247.2, 253.4, 259.6, 265.8, 272.0, 278.6, 285.2, 291.8, 298.4, 305.0, 311.2, 317.4, 323.6, 329.8, 336.0, 341.8, 347.6, 353.4, 359.2, 365.0, 370.4, 375.8, 381.2, 386.6, 392.0, 397.0, 402.0, 407.0, 412.0, 417.0, 421.6, 426.2, 430.8, 435.4, 440.0, 444.6, 449.2, 453.8, 458.4, 463.0, 467.2, 471.4]. The historical solar_power data for the past 170 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.12, 0.12]. Think about how DHI, Dew Point, GHI, Relative Humidity, DNI influence solar_power. Please give me a forecast for the next 83 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of DHI, Dew Point, GHI, Relative Humidity, DNI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_119.pkl | external_data/ground_truth_data/ground_truth_data_119.pkl | external_data/context/context_119.pkl | external_data/constraint/constraint_119.pkl |
120 | electricity_prediction-load_variability_limit | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 175 minutes. I need to manage the load variability so that it does not exceed 0.0016061966754219257 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 51 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Wind Speed data and the corresponding wind_power data for the past 175 minutes. I need to manage the load variability so that it does not exceed 0.0016061966754219257 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Temperature data for the past 175 minutes is: [21.68, 21.72, 21.76, 21.8, 21.84, 21.88, 21.92, 21.96, 22.0, 22.04, 22.08, 22.12, 22.16, 22.2, 22.24, 22.28, 22.32, 22.36, 22.4, 22.44, 22.48, 22.52, 22.56, 22.6, 22.64, 22.68, 22.72, 22.76, 22.8, 22.84, 22.88, 22.92, 22.96, 23.0, 23.04, 23.08, 23.12, 23.16, 23.2, 23.24, 23.28, 23.32, 23.36, 23.4, 23.44, 23.48, 23.52, 23.56, 23.6, 23.62, 23.64, 23.66, 23.68, 23.7, 23.72, 23.74, 23.76, 23.78, 23.8, 23.82, 23.84, 23.86, 23.88, 23.9, 23.92, 23.94, 23.96, 23.98, 24.0, 24.02, 24.04, 24.06, 24.08, 24.1, 24.12, 24.14, 24.16, 24.18, 24.2, 24.22, 24.24, 24.26, 24.28, 24.3, 24.32, 24.34, 24.36, 24.38, 24.4, 24.42, 24.44, 24.46, 24.48, 24.5, 24.52, 24.54, 24.56, 24.58, 24.6, 24.62, 24.64, 24.66, 24.68, 24.7, 24.72, 24.74, 24.76, 24.78, 24.8, 24.8, 24.8, 24.8, 24.8, 24.8, 24.82, 24.84, 24.86, 24.88, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.9, 24.92, 24.94, 24.96, 24.98, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.02, 25.04, 25.06, 25.08, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.1, 25.12, 25.14, 25.16, 25.18, 25.2, 25.2]. The historical Wind Speed data for the past 175 minutes is: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.02, 1.04, 1.06, 1.08, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.12, 1.14, 1.16, 1.18, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2]. The historical wind_power data for the past 175 minutes is: [0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]. Think about how Temperature, Wind Speed influence wind_power. Please give me a forecast for the next 51 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_120.pkl | external_data/ground_truth_data/ground_truth_data_120.pkl | external_data/context/context_120.pkl | external_data/constraint/constraint_120.pkl |
121 | electricity_prediction-load_variability_limit | I have historical Wind Speed, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 84 minutes. I need to manage the load variability so that it does not exceed 0.07356276932702076 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Wind Speed, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 74 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity, Solar Zenith Angle data and the corresponding load_power data for the past 84 minutes. I need to manage the load variability so that it does not exceed 0.07356276932702076 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Wind Speed data for the past 84 minutes is: [3.54, 3.56, 3.58, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.6, 3.62, 3.64, 3.66, 3.68, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.72, 3.74, 3.76, 3.78, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.82, 3.84, 3.86, 3.88, 3.9, 3.9, 3.9, 3.9, 3.9, 3.9, 3.92, 3.94, 3.96, 3.98, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.02, 4.04, 4.06, 4.08, 4.1, 4.1, 4.1, 4.1, 4.1, 4.1, 4.12, 4.14, 4.16, 4.18, 4.2, 4.2, 4.2, 4.2, 4.2, 4.2, 4.22, 4.24, 4.26, 4.28, 4.3]. The historical Relative Humidity data for the past 84 minutes is: [95.1, 95.22, 95.34, 95.46, 95.58, 95.71, 95.83, 95.96, 96.08, 96.2, 96.32, 96.45, 96.57, 96.69, 96.69, 96.69, 96.69, 96.69, 96.69, 96.82, 96.94, 97.07, 97.19, 97.32, 97.44, 97.57, 97.69, 97.82, 97.94, 98.07, 98.19, 98.32, 98.44, 98.57, 98.83, 99.08, 99.34, 99.59, 99.85, 99.88, 99.91, 99.94, 99.97, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 98.34, 96.67, 95.01, 93.34, 91.68, 91.92, 92.16, 92.39, 92.63, 92.87, 92.99, 93.11, 93.23, 93.35, 93.47, 93.59, 93.71, 93.84, 93.96, 94.08, 94.2, 94.32, 94.45, 94.57, 94.69, 94.81, 94.94, 95.06, 95.19, 95.31]. The historical Solar Zenith Angle data for the past 84 minutes is: [121.21, 121.09, 120.98, 120.87, 120.75, 120.64, 120.52, 120.41, 120.29, 120.17, 120.05, 119.93, 119.81, 119.69, 119.57, 119.45, 119.32, 119.2, 119.08, 118.95, 118.82, 118.7, 118.57, 118.44, 118.31, 118.18, 118.06, 117.93, 117.8, 117.67, 117.54, 117.4, 117.27, 117.14, 117.0, 116.87, 116.73, 116.6, 116.46, 116.32, 116.18, 116.05, 115.91, 115.77, 115.63, 115.49, 115.35, 115.21, 115.07, 114.93, 114.78, 114.64, 114.49, 114.35, 114.21, 114.06, 113.92, 113.77, 113.63, 113.48, 113.33, 113.19, 113.04, 112.89, 112.74, 112.59, 112.43, 112.28, 112.13, 111.98, 111.83, 111.67, 111.52, 111.37, 111.22, 111.06, 110.91, 110.75, 110.6, 110.44, 110.28, 110.13, 109.97, 109.81]. The historical load_power data for the past 84 minutes is: [0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.61, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6]. Think about how Wind Speed, Relative Humidity, Solar Zenith Angle influence load_power. Please give me a forecast for the next 74 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_121.pkl | external_data/ground_truth_data/ground_truth_data_121.pkl | external_data/context/context_121.pkl | external_data/constraint/constraint_121.pkl |
122 | electricity_prediction-load_variability_limit | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 130 minutes. I need to manage the load variability so that it does not exceed 0.03319913274019163 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 75 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 130 minutes. I need to manage the load variability so that it does not exceed 0.03319913274019163 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Wind Speed data for the past 130 minutes is: [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.02, 2.04, 2.06, 2.08, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.12, 2.14, 2.16, 2.18, 2.2, 2.22, 2.24, 2.26, 2.28, 2.3, 2.3, 2.3, 2.3, 2.3, 2.3, 2.32, 2.34, 2.36, 2.38, 2.4, 2.4, 2.4, 2.4, 2.4, 2.4, 2.42, 2.44, 2.46, 2.48, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.52, 2.54, 2.56, 2.58, 2.6, 2.62, 2.64, 2.66, 2.68, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.7, 2.72, 2.74, 2.76, 2.78, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.82, 2.84, 2.86, 2.88, 2.9, 2.9, 2.9, 2.9, 2.9, 2.9, 2.88]. The historical Relative Humidity data for the past 130 minutes is: [96.36, 96.22, 96.09, 95.95, 95.95, 95.95, 95.95, 95.95, 95.95, 95.81, 95.68, 95.54, 95.41, 95.27, 95.13, 95.0, 94.86, 94.73, 94.59, 94.46, 94.32, 94.19, 94.05, 93.92, 93.79, 93.66, 93.52, 93.39, 93.26, 93.13, 93.0, 92.86, 92.73, 92.6, 92.47, 92.34, 92.2, 92.07, 91.94, 93.13, 94.33, 95.52, 96.72, 97.91, 97.77, 97.63, 97.5, 97.36, 97.22, 97.08, 96.94, 96.81, 96.67, 96.53, 96.39, 96.26, 96.12, 95.99, 95.85, 95.72, 95.58, 95.45, 95.31, 95.18, 95.05, 94.91, 94.78, 94.64, 94.51, 94.25, 93.98, 93.72, 93.45, 93.19, 92.95, 92.7, 92.46, 92.21, 91.97, 91.71, 91.46, 91.2, 90.95, 90.69, 90.44, 90.19, 89.93, 89.68, 89.43, 89.3, 89.18, 89.05, 88.93, 88.8, 88.55, 88.31, 88.06, 87.82, 87.57, 89.75, 91.93, 94.12, 96.3, 98.48, 98.21, 97.93, 97.66, 97.38, 97.11, 96.84, 96.57, 96.31, 96.04, 95.77, 95.51, 95.24, 94.98, 94.71, 94.45, 94.19, 93.93, 93.67, 93.41, 93.15, 93.02, 92.89, 92.76, 92.63, 92.5, 92.12]. The historical wind_power data for the past 130 minutes is: [0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.08, 0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.08]. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 75 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_122.pkl | external_data/ground_truth_data/ground_truth_data_122.pkl | external_data/context/context_122.pkl | external_data/constraint/constraint_122.pkl |
123 | electricity_prediction-load_variability_limit | I have historical Dew Point, Relative Humidity, Temperature data and the corresponding load_power data for the past 165 minutes. I need to manage the load variability so that it does not exceed 0.0003131021421066106 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Dew Point, Relative Humidity, Temperature influence load_power. Please give me a forecast for the next 36 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Relative Humidity, Temperature data and the corresponding load_power data for the past 165 minutes. I need to manage the load variability so that it does not exceed 0.0003131021421066106 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Dew Point data for the past 165 minutes is: [-9.2, -9.2, -9.19, -9.19, -9.19, -9.19, -9.19, -9.19, -9.19, -9.19, -9.19, -9.18, -9.18, -9.18, -9.18, -9.18, -9.18, -9.18, -9.18, -9.18, -9.17, -9.17, -9.17, -9.17, -9.17, -9.17, -9.17, -9.17, -9.17, -9.16, -9.16, -9.16, -9.16, -9.16, -9.16, -9.16, -9.16, -9.16, -9.15, -9.15, -9.15, -9.15, -9.15, -9.15, -9.15, -9.15, -9.15, -9.14, -9.14, -9.14, -9.14, -9.14, -9.14, -9.14, -9.14, -9.14, -9.13, -9.13, -9.13, -9.13, -9.13, -9.13, -9.13, -9.13, -9.13, -9.12, -9.12, -9.12, -9.12, -9.12, -9.12, -9.12, -9.12, -9.12, -9.11, -9.11, -9.11, -9.11, -9.11, -9.11, -9.11, -9.11, -9.11, -9.1, -9.1, -9.1, -9.1, -9.1, -9.1, -9.1, -9.1, -9.1, -9.09, -9.09, -9.09, -9.09, -9.09, -9.09, -9.09, -9.09, -9.09, -9.08, -9.08, -9.08, -9.08, -9.08, -9.08, -9.08, -9.08, -9.08, -9.07, -9.07, -9.07, -9.07, -9.07, -9.07, -9.07, -9.07, -9.07, -9.06, -9.06, -9.06, -9.06, -9.06, -9.06, -9.06, -9.06, -9.06, -9.05, -9.05, -9.05, -9.05, -9.05, -9.05, -9.05, -9.05, -9.05, -9.04, -9.04, -9.04, -9.04, -9.04, -9.04, -9.04, -9.04, -9.04, -9.04, -9.03, -9.03, -9.03, -9.03, -9.03, -9.03, -9.03, -9.03, -9.03, -9.02, -9.02, -9.02, -9.02, -9.02, -9.02, -9.02, -9.02, -9.02]. The historical Relative Humidity data for the past 165 minutes is: [75.71, 75.73, 75.74, 75.75, 75.77, 75.78, 75.8, 75.81, 75.82, 75.84, 75.85, 75.86, 75.88, 75.89, 75.91, 75.92, 75.93, 75.95, 75.96, 75.98, 75.99, 76.0, 76.02, 76.03, 76.05, 76.06, 76.07, 76.09, 76.1, 76.12, 76.13, 76.14, 76.16, 76.17, 76.18, 76.2, 76.21, 76.23, 76.24, 76.25, 76.27, 76.28, 76.3, 76.31, 76.32, 76.34, 76.35, 76.37, 76.38, 76.39, 76.41, 76.42, 76.44, 76.45, 76.46, 76.48, 76.49, 76.5, 76.52, 76.53, 76.55, 76.56, 76.57, 76.59, 76.6, 76.62, 76.63, 76.64, 76.66, 76.67, 76.69, 76.7, 76.71, 76.73, 76.74, 76.75, 76.77, 76.78, 76.8, 76.81, 76.82, 76.84, 76.85, 76.87, 76.88, 76.89, 76.91, 76.92, 76.94, 76.95, 76.96, 76.98, 76.99, 77.01, 77.02, 77.03, 77.05, 77.06, 77.07, 77.09, 77.1, 77.12, 77.13, 77.14, 77.16, 77.17, 77.19, 77.2, 77.21, 77.23, 77.24, 77.26, 77.27, 77.28, 77.3, 77.31, 77.33, 77.34, 77.35, 77.37, 77.38, 77.39, 77.41, 77.42, 77.44, 77.45, 77.46, 77.48, 77.49, 77.51, 77.52, 77.53, 77.55, 77.56, 77.58, 77.59, 77.6, 77.62, 77.63, 77.65, 77.66, 77.67, 77.69, 77.7, 77.71, 77.73, 77.74, 77.76, 77.77, 77.78, 77.8, 77.81, 77.83, 77.84, 77.85, 77.87, 77.88, 77.9, 77.91, 77.92, 77.94, 77.95, 77.97, 77.98, 77.99]. The historical Temperature data for the past 165 minutes is: [-5.6, -5.6, -5.6, -5.61, -5.61, -5.61, -5.61, -5.61, -5.61, -5.61, -5.61, -5.61, -5.61, -5.62, -5.62, -5.62, -5.62, -5.62, -5.62, -5.62, -5.62, -5.62, -5.62, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.63, -5.64, -5.64, -5.64, -5.64, -5.64, -5.64, -5.64, -5.64, -5.64, -5.64, -5.65, -5.65, -5.65, -5.65, -5.65, -5.65, -5.65, -5.65, -5.65, -5.65, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.66, -5.67, -5.67, -5.67, -5.67, -5.67, -5.67, -5.67, -5.67, -5.67, -5.67, -5.68, -5.68, -5.68, -5.68, -5.68, -5.68, -5.68, -5.68, -5.68, -5.68, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.69, -5.7, -5.7, -5.7, -5.7, -5.7, -5.7, -5.7, -5.7, -5.7, -5.7, -5.71, -5.71, -5.71, -5.71, -5.71, -5.71, -5.71, -5.71, -5.71, -5.71, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.72, -5.73, -5.73, -5.73, -5.73, -5.73, -5.73, -5.73, -5.73, -5.73, -5.73, -5.74, -5.74, -5.74, -5.74, -5.74, -5.74, -5.74, -5.74, -5.74, -5.74, -5.75, -5.75, -5.75, -5.75, -5.75, -5.75, -5.75, -5.75, -5.75, -5.75, -5.76, -5.76, -5.76, -5.76, -5.76, -5.76, -5.76, -5.76]. The historical load_power data for the past 165 minutes is: [0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93, 0.93]. Think about how Dew Point, Relative Humidity, Temperature influence load_power. Please give me a forecast for the next 36 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_123.pkl | external_data/ground_truth_data/ground_truth_data_123.pkl | external_data/context/context_123.pkl | external_data/constraint/constraint_123.pkl |
124 | electricity_prediction-load_variability_limit | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 84 minutes. I need to manage the load variability so that it does not exceed 0.02692045398077515 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 54 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature data and the corresponding wind_power data for the past 84 minutes. I need to manage the load variability so that it does not exceed 0.02692045398077515 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Relative Humidity data for the past 84 minutes is: [52.22, 52.14, 52.05, 51.96, 51.96, 51.96, 51.96, 51.96, 51.96, 51.89, 51.82, 51.75, 51.68, 51.61, 51.54, 51.47, 51.4, 51.33, 51.26, 51.19, 51.12, 51.05, 50.98, 50.91, 50.83, 50.75, 50.67, 50.59, 50.51, 50.44, 50.37, 50.31, 50.24, 50.17, 50.1, 50.03, 49.97, 49.9, 49.83, 49.76, 49.69, 49.63, 49.56, 49.49, 49.42, 49.35, 49.29, 49.22, 49.15, 49.08, 49.02, 48.95, 48.89, 48.82, 48.75, 48.69, 48.62, 48.56, 48.49, 49.24, 49.99, 50.73, 51.48, 52.23, 52.16, 52.09, 52.02, 51.95, 51.88, 51.8, 51.72, 51.64, 51.56, 51.48, 51.41, 51.34, 51.27, 51.2, 51.13, 51.06, 50.99, 50.92, 50.85, 50.78]. The historical Temperature data for the past 84 minutes is: [6.84, 6.86, 6.88, 6.9, 6.9, 6.9, 6.9, 6.9, 6.9, 6.92, 6.94, 6.96, 6.98, 7.0, 7.02, 7.04, 7.06, 7.08, 7.1, 7.12, 7.14, 7.16, 7.18, 7.2, 7.22, 7.24, 7.26, 7.28, 7.3, 7.32, 7.34, 7.36, 7.38, 7.4, 7.42, 7.44, 7.46, 7.48, 7.5, 7.52, 7.54, 7.56, 7.58, 7.6, 7.62, 7.64, 7.66, 7.68, 7.7, 7.72, 7.74, 7.76, 7.78, 7.8, 7.82, 7.84, 7.86, 7.88, 7.9, 7.92, 7.94, 7.96, 7.98, 8.0, 8.02, 8.04, 8.06, 8.08, 8.1, 8.12, 8.14, 8.16, 8.18, 8.2, 8.22, 8.24, 8.26, 8.28, 8.3, 8.32, 8.34, 8.36, 8.38, 8.4]. The historical wind_power data for the past 84 minutes is: [0.12, 0.12, 0.12, 0.13, 0.12, 0.12, 0.11, 0.11, 0.11, 0.11, 0.12, 0.12, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.15, 0.15, 0.15, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.18]. Think about how Relative Humidity, Temperature influence wind_power. Please give me a forecast for the next 54 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_124.pkl | external_data/ground_truth_data/ground_truth_data_124.pkl | external_data/context/context_124.pkl | external_data/constraint/constraint_124.pkl |
125 | electricity_prediction-load_variability_limit | I have historical Wind Speed, Dew Point, Temperature data and the corresponding load_power data for the past 152 minutes. I need to manage the load variability so that it does not exceed 0.005762542711417935 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Wind Speed, Dew Point, Temperature influence load_power. Please give me a forecast for the next 73 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Dew Point, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Dew Point, Temperature data and the corresponding load_power data for the past 152 minutes. I need to manage the load variability so that it does not exceed 0.005762542711417935 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Wind Speed data for the past 152 minutes is: [0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]. The historical Dew Point data for the past 152 minutes is: [-15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.64, -15.68, -15.72, -15.76, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.8, -15.76, -15.72, -15.68, -15.64, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6, -15.6]. The historical Temperature data for the past 152 minutes is: [-12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.9, -12.88, -12.86, -12.84, -12.82, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.8, -12.78, -12.76, -12.74, -12.72, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.7, -12.68, -12.66, -12.64, -12.62, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.6, -12.58, -12.56, -12.54, -12.52, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.5, -12.48, -12.46, -12.44, -12.42, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.4, -12.38, -12.36, -12.34, -12.32, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.3, -12.28, -12.26, -12.24, -12.22, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2, -12.2]. The historical load_power data for the past 152 minutes is: [1.08, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.08, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07, 1.07]. Think about how Wind Speed, Dew Point, Temperature influence load_power. Please give me a forecast for the next 73 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Dew Point, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_125.pkl | external_data/ground_truth_data/ground_truth_data_125.pkl | external_data/context/context_125.pkl | external_data/constraint/constraint_125.pkl |
126 | electricity_prediction-load_variability_limit | I have historical Dew Point, Temperature, DHI, DNI, Relative Humidity data and the corresponding solar_power data for the past 162 minutes. I need to manage the load variability so that it does not exceed 0.06856309997539514 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Dew Point, Temperature, DHI, DNI, Relative Humidity influence solar_power. Please give me a forecast for the next 24 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Temperature, DHI, DNI, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Dew Point, Temperature, DHI, DNI, Relative Humidity data and the corresponding solar_power data for the past 162 minutes. I need to manage the load variability so that it does not exceed 0.06856309997539514 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Dew Point data for the past 162 minutes is: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.86, 0.72, 0.58, 0.44, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, -0.02, -0.34, -0.66, -0.98, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3, -1.3]. The historical Temperature data for the past 162 minutes is: [10.88, 10.92, 10.96, 11.0, 11.04, 11.08, 11.12, 11.16, 11.2, 11.22, 11.24, 11.26, 11.28, 11.3, 11.34, 11.38, 11.42, 11.46, 11.5, 11.56, 11.62, 11.68, 11.74, 11.8, 11.84, 11.88, 11.92, 11.96, 12.0, 12.06, 12.12, 12.18, 12.24, 12.3, 12.36, 12.42, 12.48, 12.54, 12.6, 12.66, 12.72, 12.78, 12.84, 12.9, 12.96, 13.02, 13.08, 13.14, 13.2, 13.24, 13.28, 13.32, 13.36, 13.4, 13.46, 13.52, 13.58, 13.64, 13.7, 13.76, 13.82, 13.88, 13.94, 14.0, 14.06, 14.12, 14.18, 14.24, 14.3, 14.36, 14.42, 14.48, 14.54, 14.6, 14.64, 14.68, 14.72, 14.76, 14.8, 14.88, 14.96, 15.04, 15.12, 15.2, 15.28, 15.36, 15.44, 15.52, 15.6, 15.68, 15.76, 15.84, 15.92, 16.0, 16.06, 16.12, 16.18, 16.24, 16.3, 16.38, 16.46, 16.54, 16.62, 16.7, 16.78, 16.86, 16.94, 17.02, 17.1, 17.16, 17.22, 17.28, 17.34, 17.4, 17.48, 17.56, 17.64, 17.72, 17.8, 17.88, 17.96, 18.04, 18.12, 18.2, 18.28, 18.36, 18.44, 18.52, 18.6, 18.66, 18.72, 18.78, 18.84, 18.9, 18.98, 19.06, 19.14, 19.22, 19.3, 19.34, 19.38, 19.42, 19.46, 19.5, 19.54, 19.58, 19.62, 19.66, 19.7, 19.74, 19.78, 19.82, 19.86, 19.9, 19.94, 19.98, 20.02, 20.06, 20.1, 20.14, 20.18, 20.22]. The historical DHI data for the past 162 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2, 4.4, 6.6, 8.8, 11.0, 11.2, 11.4, 11.6, 11.8, 12.0, 13.4, 14.8, 16.2, 17.6, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.2, 26.4, 27.6, 28.8, 30.0, 31.4, 32.8, 34.2, 35.6, 37.0, 36.0, 35.0, 34.0, 33.0, 32.0, 35.6, 39.2, 42.8, 46.4, 50.0, 47.4, 44.8, 42.2, 39.6, 37.0, 42.2, 47.4, 52.6, 57.8, 63.0, 64.2, 65.4, 66.6, 67.8, 69.0, 64.0, 59.0, 54.0, 49.0, 44.0, 51.6, 59.2, 66.8, 74.4, 82.0, 83.2, 84.4, 85.6, 86.8, 88.0, 80.2, 72.4, 64.6, 56.8, 49.0, 49.4, 49.8, 50.2, 50.6, 51.0, 51.2, 51.4, 51.6, 51.8, 52.0, 52.4, 52.8, 53.2, 53.6, 54.0, 54.2, 54.4, 54.6, 54.8, 55.0, 55.4, 55.8, 56.2, 56.6, 57.0, 57.2, 57.4, 57.6, 57.8, 58.0, 58.4, 58.8, 59.2, 59.6, 60.0, 60.2, 60.4, 60.6, 60.8, 61.0, 61.2, 61.4, 61.6, 61.8, 62.0, 62.2, 62.4, 62.6, 62.8, 63.0, 63.2, 63.4, 63.6, 63.8, 64.0, 64.2, 64.4, 64.6, 64.8, 65.0, 65.2, 65.4, 65.6, 65.8, 66.0, 66.2, 66.4, 66.6, 66.8, 67.0, 67.2, 67.4, 67.6, 67.8, 68.0, 68.2, 68.4, 68.6]. The historical DNI data for the past 162 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 37.0, 74.0, 111.0, 148.0, 185.0, 148.0, 111.0, 74.0, 37.0, 0.0, 1.8, 3.6, 5.4, 7.2, 9.0, 17.6, 26.2, 34.8, 43.4, 52.0, 58.4, 64.8, 71.2, 77.6, 84.0, 89.2, 94.4, 99.6, 104.8, 110.0, 197.2, 284.4, 371.6, 458.8, 546.0, 467.8, 389.6, 311.4, 233.2, 155.0, 247.2, 339.4, 431.6, 523.8, 616.0, 532.4, 448.8, 365.2, 281.6, 198.0, 202.0, 206.0, 210.0, 214.0, 218.0, 314.4, 410.8, 507.2, 603.6, 700.0, 611.0, 522.0, 433.0, 344.0, 255.0, 258.4, 261.8, 265.2, 268.6, 272.0, 370.2, 468.4, 566.6, 664.8, 763.0, 766.6, 770.2, 773.8, 777.4, 781.0, 784.4, 787.8, 791.2, 794.6, 798.0, 801.0, 804.0, 807.0, 810.0, 813.0, 815.8, 818.6, 821.4, 824.2, 827.0, 829.6, 832.2, 834.8, 837.4, 840.0, 842.0, 844.0, 846.0, 848.0, 850.0, 852.4, 854.8, 857.2, 859.6, 862.0, 864.0, 866.0, 868.0, 870.0, 872.0, 874.2, 876.4, 878.6, 880.8, 883.0, 884.8, 886.6, 888.4, 890.2, 892.0, 893.8, 895.6, 897.4, 899.2, 901.0, 902.6, 904.2, 905.8, 907.4, 909.0, 910.6, 912.2, 913.8, 915.4, 917.0, 918.6, 920.2, 921.8, 923.4, 925.0, 926.4, 927.8, 929.2, 930.6, 932.0, 933.2, 934.4, 935.6]. The historical Relative Humidity data for the past 162 minutes is: [50.45, 50.32, 50.18, 50.05, 49.92, 49.79, 49.65, 49.52, 49.39, 49.33, 49.26, 49.2, 49.13, 49.07, 48.94, 48.81, 48.68, 48.55, 48.42, 48.23, 48.04, 47.86, 47.67, 47.48, 47.36, 47.23, 47.11, 46.98, 46.86, 46.68, 46.49, 46.31, 46.12, 45.94, 45.76, 45.58, 45.41, 45.23, 45.05, 44.88, 44.7, 44.53, 44.35, 44.18, 44.01, 43.84, 43.66, 43.49, 43.32, 42.81, 42.29, 41.78, 41.26, 40.75, 40.59, 40.43, 40.28, 40.12, 39.96, 39.81, 39.67, 39.52, 39.38, 39.23, 39.08, 38.93, 38.78, 38.63, 38.48, 38.33, 38.18, 38.04, 37.89, 37.74, 37.64, 37.55, 37.45, 37.36, 37.26, 37.07, 36.88, 36.7, 36.51, 36.32, 36.14, 35.95, 35.77, 35.58, 35.4, 35.22, 35.04, 34.87, 34.69, 34.51, 34.38, 34.25, 34.12, 33.99, 33.86, 33.69, 33.52, 33.35, 33.18, 33.01, 32.85, 32.68, 32.52, 32.35, 32.19, 31.37, 30.55, 29.72, 28.9, 28.08, 27.94, 27.8, 27.67, 27.53, 27.39, 27.25, 27.12, 26.98, 26.85, 26.71, 26.58, 26.45, 26.31, 26.18, 26.05, 25.95, 25.86, 25.76, 25.67, 25.57, 25.44, 25.32, 25.19, 25.07, 24.94, 24.88, 24.82, 24.75, 24.69, 24.63, 24.57, 24.51, 24.45, 24.39, 24.33, 24.27, 24.21, 24.15, 24.09, 24.03, 23.97, 23.91, 23.86, 23.8, 23.74, 23.68, 23.62, 23.56]. The historical solar_power data for the past 162 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.04, 0.05, 0.06, 0.06, 0.07, 0.07, 0.06, 0.06, 0.06, 0.05, 0.06, 0.08, 0.09, 0.1, 0.11, 0.1, 0.1, 0.09, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.09, 0.11, 0.12, 0.14, 0.15, 0.17, 0.16, 0.15, 0.14, 0.13, 0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.15, 0.17, 0.19, 0.21, 0.23, 0.23, 0.24, 0.24, 0.25, 0.25, 0.26, 0.26, 0.26, 0.27, 0.27, 0.28, 0.28, 0.28, 0.29, 0.29, 0.3, 0.3, 0.3, 0.31, 0.31, 0.32, 0.32, 0.32, 0.33, 0.33, 0.34, 0.34, 0.34, 0.35, 0.35, 0.35, 0.36, 0.36, 0.37, 0.37, 0.37, 0.38, 0.38, 0.39, 0.39, 0.39, 0.4, 0.4, 0.4, 0.41, 0.41, 0.42, 0.42, 0.42, 0.43, 0.43, 0.43, 0.44, 0.44, 0.44, 0.45, 0.45, 0.46, 0.46, 0.46, 0.47, 0.47, 0.47, 0.48, 0.48, 0.48, 0.49, 0.49, 0.49, 0.5, 0.5, 0.5, 0.51, 0.51, 0.51, 0.52, 0.52, 0.52]. Think about how Dew Point, Temperature, DHI, DNI, Relative Humidity influence solar_power. Please give me a forecast for the next 24 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Dew Point, Temperature, DHI, DNI, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_126.pkl | external_data/ground_truth_data/ground_truth_data_126.pkl | external_data/context/context_126.pkl | external_data/constraint/constraint_126.pkl |
127 | electricity_prediction-load_variability_limit | I have historical Relative Humidity, Temperature, Solar Zenith Angle data and the corresponding load_power data for the past 144 minutes. I need to manage the load variability so that it does not exceed 0.010582641301406542 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Relative Humidity, Temperature, Solar Zenith Angle influence load_power. Please give me a forecast for the next 75 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Temperature, Solar Zenith Angle data and the corresponding load_power data for the past 144 minutes. I need to manage the load variability so that it does not exceed 0.010582641301406542 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Relative Humidity data for the past 144 minutes is: [90.65, 90.53, 90.51, 90.49, 90.48, 90.46, 90.44, 90.32, 90.21, 90.09, 89.98, 89.86, 90.07, 90.27, 90.48, 90.68, 90.89, 90.89, 90.89, 90.89, 90.89, 90.89, 90.77, 90.65, 90.54, 90.42, 90.3, 90.18, 90.07, 89.95, 89.84, 89.72, 89.72, 89.72, 89.72, 89.72, 89.72, 89.6, 89.49, 89.37, 89.26, 89.14, 89.14, 89.14, 89.14, 89.14, 89.14, 89.03, 88.91, 88.8, 88.68, 88.57, 88.57, 88.57, 88.57, 88.57, 88.57, 88.46, 88.34, 88.23, 88.11, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 87.89, 87.78, 87.66, 87.55, 87.44, 87.71, 87.98, 88.24, 88.51, 88.78, 88.67, 88.55, 88.44, 88.32, 88.21, 88.1, 87.99, 87.87, 87.76, 87.65, 87.65, 87.65, 87.65, 87.65, 87.65, 87.54, 87.43, 87.31, 87.2, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 87.09, 86.96, 86.83, 86.7, 86.57, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.44, 86.5, 86.56, 86.62, 86.68, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74, 86.74]. The historical Temperature data for the past 144 minutes is: [13.88, 13.9, 13.9, 13.9, 13.9, 13.9, 13.9, 13.92, 13.94, 13.96, 13.98, 14.0, 14.02, 14.04, 14.06, 14.08, 14.1, 14.1, 14.1, 14.1, 14.1, 14.1, 14.12, 14.14, 14.16, 14.18, 14.2, 14.22, 14.24, 14.26, 14.28, 14.3, 14.3, 14.3, 14.3, 14.3, 14.3, 14.32, 14.34, 14.36, 14.38, 14.4, 14.4, 14.4, 14.4, 14.4, 14.4, 14.42, 14.44, 14.46, 14.48, 14.5, 14.5, 14.5, 14.5, 14.5, 14.5, 14.52, 14.54, 14.56, 14.58, 14.6, 14.6, 14.6, 14.6, 14.6, 14.6, 14.62, 14.64, 14.66, 14.68, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.72, 14.74, 14.76, 14.78, 14.8, 14.82, 14.84, 14.86, 14.88, 14.9, 14.9, 14.9, 14.9, 14.9, 14.9, 14.92, 14.94, 14.96, 14.98, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.02, 15.04, 15.06, 15.08, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1, 15.1]. The historical Solar Zenith Angle data for the past 144 minutes is: [33.04, 32.9, 32.77, 32.64, 32.5, 32.37, 32.24, 32.11, 31.98, 31.86, 31.73, 31.6, 31.48, 31.36, 31.23, 31.11, 30.99, 30.87, 30.76, 30.64, 30.53, 30.41, 30.3, 30.19, 30.07, 29.96, 29.85, 29.74, 29.64, 29.53, 29.43, 29.32, 29.22, 29.12, 29.03, 28.93, 28.83, 28.74, 28.64, 28.55, 28.45, 28.36, 28.27, 28.19, 28.1, 28.02, 27.93, 27.85, 27.77, 27.7, 27.62, 27.54, 27.47, 27.4, 27.32, 27.25, 27.18, 27.12, 27.05, 26.99, 26.92, 26.86, 26.8, 26.75, 26.69, 26.64, 26.58, 26.53, 26.49, 26.44, 26.4, 26.35, 26.31, 26.27, 26.24, 26.2, 26.16, 26.13, 26.1, 26.07, 26.04, 26.01, 25.99, 25.97, 25.94, 25.92, 25.9, 25.89, 25.88, 25.86, 25.85, 25.84, 25.84, 25.84, 25.83, 25.83, 25.83, 25.84, 25.84, 25.85, 25.85, 25.86, 25.88, 25.89, 25.91, 25.92, 25.94, 25.96, 25.99, 26.01, 26.04, 26.06, 26.09, 26.13, 26.16, 26.2, 26.23, 26.27, 26.31, 26.36, 26.4, 26.44, 26.49, 26.54, 26.6, 26.65, 26.7, 26.76, 26.82, 26.87, 26.93, 26.99, 27.06, 27.12, 27.19, 27.25, 27.32, 27.4, 27.47, 27.55, 27.62, 27.7, 27.78, 27.86]. The historical load_power data for the past 144 minutes is: [0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88, 0.88]. Think about how Relative Humidity, Temperature, Solar Zenith Angle influence load_power. Please give me a forecast for the next 75 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Temperature, Solar Zenith Angle are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_127.pkl | external_data/ground_truth_data/ground_truth_data_127.pkl | external_data/context/context_127.pkl | external_data/constraint/constraint_127.pkl |
128 | electricity_prediction-load_variability_limit | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 176 minutes. I need to manage the load variability so that it does not exceed 0.01585411439615748 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 47 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 176 minutes. I need to manage the load variability so that it does not exceed 0.01585411439615748 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Temperature data for the past 176 minutes is: [5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.38, 5.36, 5.34, 5.32, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.28, 5.26, 5.24, 5.22, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.18, 5.16, 5.14, 5.12, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.08, 5.06, 5.04, 5.02, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.98, 4.96, 4.94, 4.92, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.9, 4.88, 4.86, 4.84, 4.82, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.8, 4.78, 4.76, 4.74, 4.72, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.7, 4.68, 4.66, 4.64, 4.62, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6, 4.6]. The historical Relative Humidity data for the past 176 minutes is: [63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.19, 63.28, 63.37, 63.45, 63.54, 63.63, 63.63, 63.63, 63.63, 63.63, 63.63, 63.62, 63.6, 63.59, 63.57, 63.56, 63.56, 63.56, 63.56, 63.56, 63.56, 63.65, 63.74, 63.82, 63.91, 64.0, 64.0, 64.0, 64.0, 64.0, 64.0, 64.19, 64.37, 64.56, 64.74, 64.93, 65.02, 65.11, 65.2, 65.29, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.38, 65.47, 65.56, 65.66, 65.75, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.84, 65.93, 66.02, 66.12, 66.21, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.3, 66.64, 66.98, 67.32, 67.66, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.0, 68.09, 68.19, 68.28, 68.38, 68.47, 68.47, 68.47, 68.47, 68.47, 68.47, 68.46, 68.44, 68.43, 68.41, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.4, 68.5, 68.59, 68.69, 68.78, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 68.88, 69.19, 69.49, 69.8, 70.1, 70.41, 70.41, 70.41, 70.41]. The historical wind_power data for the past 176 minutes is: [0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.12, 0.12, 0.13, 0.13, 0.14, 0.15, 0.15, 0.15, 0.15, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.12, 0.13, 0.13, 0.13, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 0.13, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.13, 0.13, 0.13, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.11, 0.12, 0.12, 0.13, 0.13, 0.13, 0.13, 0.13, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.13, 0.12, 0.12, 0.12, 0.12, 0.11, 0.12, 0.12, 0.12]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 47 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_128.pkl | external_data/ground_truth_data/ground_truth_data_128.pkl | external_data/context/context_128.pkl | external_data/constraint/constraint_128.pkl |
129 | electricity_prediction-load_variability_limit | I have historical Temperature, DHI, Relative Humidity, Dew Point, DNI data and the corresponding solar_power data for the past 66 minutes. I need to manage the load variability so that it does not exceed 0.0479598887015956 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Temperature, DHI, Relative Humidity, Dew Point, DNI influence solar_power. Please give me a forecast for the next 65 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Temperature, DHI, Relative Humidity, Dew Point, DNI are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, DHI, Relative Humidity, Dew Point, DNI data and the corresponding solar_power data for the past 66 minutes. I need to manage the load variability so that it does not exceed 0.0479598887015956 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Temperature data for the past 66 minutes is: [0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.72, 0.74, 0.76, 0.78, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.82, 0.84, 0.86, 0.88, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.92, 0.94]. The historical DHI data for the past 66 minutes is: [48.4, 53.6, 58.8, 64.0, 63.4, 62.8, 62.2, 61.6, 61.0, 62.4, 63.8, 65.2, 66.6, 68.0, 67.4, 66.8, 66.2, 65.6, 65.0, 65.4, 65.8, 66.2, 66.6, 67.0, 76.0, 85.0, 94.0, 103.0, 112.0, 112.2, 112.4, 112.6, 112.8, 113.0, 115.8, 118.6, 121.4, 124.2, 127.0, 129.0, 131.0, 133.0, 135.0, 137.0, 132.8, 128.6, 124.4, 120.2, 116.0, 113.4, 110.8, 108.2, 105.6, 103.0, 101.4, 99.8, 98.2, 96.6, 95.0, 101.6, 108.2, 114.8, 121.4, 128.0, 128.0, 128.0]. The historical Relative Humidity data for the past 66 minutes is: [81.69, 81.58, 81.46, 81.34, 81.22, 81.1, 80.99, 80.87, 80.75, 81.83, 82.9, 83.98, 85.05, 86.13, 86.13, 86.13, 86.13, 86.13, 86.13, 86.01, 85.88, 85.76, 85.63, 85.51, 85.39, 85.26, 85.14, 85.01, 84.89, 84.75, 84.61, 84.48, 84.34, 84.2, 84.08, 83.96, 83.83, 83.71, 83.59, 83.59, 83.59, 83.59, 83.59, 83.59, 83.47, 83.35, 83.23, 83.11, 82.99, 82.99, 82.99, 82.99, 82.99, 82.99, 82.87, 82.75, 82.64, 82.52, 82.4, 82.4, 82.4, 82.4, 82.4, 82.4, 82.28, 82.16]. The historical Dew Point data for the past 66 minutes is: [-2.7, -2.7, -2.7, -2.7, -2.7, -2.7, -2.7, -2.7, -2.7, -2.5, -2.3, -2.1, -1.9, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.7, -1.72, -1.74, -1.76, -1.78, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8, -1.8]. The historical DNI data for the past 66 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4, 0.8, 1.2, 1.6, 2.0, 4.2, 6.4, 8.6, 10.8, 13.0, 10.4, 7.8, 5.2, 2.6, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. The historical solar_power data for the past 66 minutes is: [0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.07, 0.08, 0.08, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.11, 0.12, 0.12, 0.12, 0.12, 0.11, 0.11, 0.1, 0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.09, 0.09, 0.1, 0.11, 0.11, 0.11]. Think about how Temperature, DHI, Relative Humidity, Dew Point, DNI influence solar_power. Please give me a forecast for the next 65 minutes for solar_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and solar_power are saved in variable VAL with last column being the target variable and future data of Temperature, DHI, Relative Humidity, Dew Point, DNI are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_129.pkl | external_data/ground_truth_data/ground_truth_data_129.pkl | external_data/context/context_129.pkl | external_data/constraint/constraint_129.pkl |
130 | electricity_prediction-load_variability_limit | I have historical Wind Speed, Solar Zenith Angle, Temperature data and the corresponding load_power data for the past 172 minutes. I need to manage the load variability so that it does not exceed 0.027398543384920145 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Wind Speed, Solar Zenith Angle, Temperature influence load_power. Please give me a forecast for the next 27 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Solar Zenith Angle, Temperature are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Solar Zenith Angle, Temperature data and the corresponding load_power data for the past 172 minutes. I need to manage the load variability so that it does not exceed 0.027398543384920145 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Wind Speed data for the past 172 minutes is: [5.0, 5.0, 5.0, 5.0, 5.0, 5.02, 5.04, 5.06, 5.08, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.1, 5.12, 5.14, 5.16, 5.18, 5.2, 5.2, 5.2, 5.2, 5.2, 5.2, 5.22, 5.24, 5.26, 5.28, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.32, 5.34, 5.36, 5.38, 5.4, 5.4, 5.4, 5.4, 5.4, 5.4, 5.36, 5.32, 5.28, 5.24, 5.2, 5.18, 5.16, 5.14, 5.12, 5.1, 5.06, 5.02, 4.98, 4.94, 4.9, 4.88, 4.86, 4.84, 4.82, 4.8, 4.76, 4.72, 4.68, 4.64, 4.6, 4.56, 4.52, 4.48, 4.44, 4.4, 4.38, 4.36, 4.34, 4.32, 4.3, 4.26, 4.22, 4.18, 4.14, 4.1, 4.06, 4.02, 3.98, 3.94, 3.9, 3.88, 3.86, 3.84, 3.82, 3.8, 3.76, 3.72, 3.68, 3.64, 3.6, 3.58, 3.56, 3.54, 3.52, 3.5, 3.46, 3.42, 3.38, 3.34, 3.3, 3.28, 3.26, 3.24, 3.22, 3.2, 3.18, 3.16, 3.14, 3.12, 3.1, 3.06, 3.02, 2.98, 2.94, 2.9, 2.88, 2.86, 2.84, 2.82, 2.8, 2.78, 2.76, 2.74, 2.72, 2.7, 2.68, 2.66, 2.64, 2.62, 2.6, 2.56, 2.52, 2.48, 2.44, 2.4, 2.38, 2.36, 2.34, 2.32, 2.3, 2.28, 2.26, 2.24, 2.22, 2.2, 2.16, 2.12, 2.08, 2.04, 2.0, 1.98, 1.96, 1.94, 1.92, 1.9, 1.9, 1.9]. The historical Solar Zenith Angle data for the past 172 minutes is: [156.31, 156.11, 155.9, 155.7, 155.5, 155.3, 155.1, 154.89, 154.69, 154.49, 154.29, 154.08, 153.88, 153.67, 153.47, 153.26, 153.06, 152.85, 152.65, 152.44, 152.23, 152.03, 151.82, 151.62, 151.41, 151.2, 150.99, 150.79, 150.58, 150.37, 150.16, 149.95, 149.75, 149.54, 149.33, 149.12, 148.91, 148.71, 148.5, 148.29, 148.08, 147.87, 147.67, 147.46, 147.25, 147.04, 146.83, 146.62, 146.41, 146.2, 145.99, 145.78, 145.57, 145.36, 145.15, 144.94, 144.73, 144.52, 144.31, 144.1, 143.89, 143.68, 143.47, 143.26, 143.05, 142.84, 142.63, 142.42, 142.21, 142.0, 141.79, 141.58, 141.37, 141.16, 140.95, 140.74, 140.53, 140.31, 140.1, 139.89, 139.68, 139.47, 139.26, 139.05, 138.84, 138.63, 138.42, 138.2, 137.99, 137.78, 137.57, 137.36, 137.15, 136.94, 136.73, 136.52, 136.31, 136.09, 135.88, 135.67, 135.46, 135.25, 135.04, 134.83, 134.62, 134.41, 134.2, 133.99, 133.78, 133.57, 133.36, 133.15, 132.93, 132.72, 132.51, 132.3, 132.09, 131.88, 131.67, 131.46, 131.25, 131.04, 130.83, 130.62, 130.41, 130.2, 129.99, 129.78, 129.57, 129.36, 129.15, 128.94, 128.73, 128.52, 128.31, 128.1, 127.89, 127.68, 127.47, 127.26, 127.05, 126.84, 126.63, 126.42, 126.21, 126.0, 125.79, 125.58, 125.37, 125.16, 124.95, 124.74, 124.53, 124.32, 124.11, 123.9, 123.69, 123.49, 123.28, 123.07, 122.86, 122.65, 122.44, 122.23, 122.02, 121.81, 121.6, 121.4, 121.19, 120.98, 120.77, 120.56]. The historical Temperature data for the past 172 minutes is: [13.4, 13.4, 13.4, 13.4, 13.4, 13.38, 13.36, 13.34, 13.32, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.3, 13.28, 13.26, 13.24, 13.22, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.2, 13.18, 13.16, 13.14, 13.12, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.1, 13.08, 13.06, 13.04, 13.02, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 12.98, 12.96, 12.94, 12.92, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.9, 12.88, 12.86, 12.84, 12.82, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.78, 12.76, 12.74, 12.72, 12.7, 12.7, 12.7, 12.7, 12.7, 12.7, 12.68, 12.66, 12.64, 12.62, 12.6, 12.58, 12.56, 12.54, 12.52, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.48, 12.46, 12.44, 12.42, 12.4, 12.38, 12.36, 12.34, 12.32, 12.3, 12.28, 12.26, 12.24, 12.22, 12.2, 12.2, 12.2, 12.2, 12.2, 12.2, 12.18, 12.16, 12.14, 12.12, 12.1, 12.08, 12.06, 12.04, 12.02, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 11.98, 11.96, 11.94, 11.92, 11.9, 11.9, 11.9]. The historical load_power data for the past 172 minutes is: [0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.68, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.69, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.71, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72, 0.72]. Think about how Wind Speed, Solar Zenith Angle, Temperature influence load_power. Please give me a forecast for the next 27 minutes for load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and load_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Solar Zenith Angle, Temperature are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_130.pkl | external_data/ground_truth_data/ground_truth_data_130.pkl | external_data/context/context_130.pkl | external_data/constraint/constraint_130.pkl |
131 | electricity_prediction-load_variability_limit | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 178 minutes. I need to manage the load variability so that it does not exceed 0.07236155346467536 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 77 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Temperature, Relative Humidity data and the corresponding wind_power data for the past 178 minutes. I need to manage the load variability so that it does not exceed 0.07236155346467536 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Temperature data for the past 178 minutes is: [19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.22, 19.24, 19.26, 19.28, 19.3, 19.32, 19.34, 19.36, 19.38, 19.4, 19.42, 19.44, 19.46, 19.48, 19.5, 19.52, 19.54, 19.56, 19.58, 19.6, 19.64, 19.68, 19.72, 19.76, 19.8, 19.82, 19.84, 19.86, 19.88, 19.9, 19.92, 19.94, 19.96, 19.98, 20.0, 20.02, 20.04, 20.06, 20.08, 20.1, 20.14, 20.18, 20.22, 20.26, 20.3, 20.32, 20.34, 20.36, 20.38, 20.4, 20.42, 20.44, 20.46, 20.48, 20.5, 20.54, 20.58, 20.62, 20.66, 20.7, 20.72, 20.74, 20.76, 20.78, 20.8, 20.84, 20.88, 20.92, 20.96, 21.0, 21.06, 21.12, 21.18, 21.24, 21.3, 21.34, 21.38, 21.42, 21.46, 21.5, 21.56, 21.62, 21.68, 21.74, 21.8, 21.84, 21.88, 21.92, 21.96, 22.0, 22.06, 22.12, 22.18, 22.24, 22.3, 22.34, 22.38, 22.42, 22.46, 22.5, 22.56, 22.62, 22.68, 22.74, 22.8, 22.84, 22.88, 22.92, 22.96, 23.0, 23.06, 23.12, 23.18, 23.24, 23.3, 23.34, 23.38, 23.42, 23.46, 23.5, 23.56, 23.62, 23.68, 23.74, 23.8, 23.84, 23.88, 23.92, 23.96, 24.0, 24.02, 24.04, 24.06, 24.08, 24.1, 24.14, 24.18, 24.22, 24.26, 24.3, 24.34, 24.38]. The historical Relative Humidity data for the past 178 minutes is: [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 99.97, 99.93, 99.9, 99.86, 99.83, 99.86, 99.9, 99.93, 99.97, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 99.95, 99.9, 99.84, 99.79, 99.74, 99.62, 99.49, 99.37, 99.24, 99.12, 98.88, 98.64, 98.4, 98.16, 97.92, 97.8, 97.68, 97.56, 97.44, 97.32, 97.08, 96.84, 96.61, 96.37, 96.13, 95.78, 95.43, 95.08, 94.73, 94.38, 94.15, 93.92, 93.7, 93.47, 93.24, 92.9, 92.56, 92.23, 91.89, 91.55, 91.33, 91.11, 90.89, 90.67, 90.45, 90.12, 89.79, 89.47, 89.14, 88.81, 88.74, 88.67, 88.59, 88.52, 88.45, 88.13, 87.81, 87.5, 87.18, 86.86, 86.65, 86.44, 86.24, 86.03, 85.82, 85.51, 85.21, 84.9, 84.6, 84.29, 84.09, 83.89, 83.68, 83.48, 83.28, 82.98, 82.68, 82.39, 82.09, 81.79, 81.6, 81.4, 81.21, 81.01, 80.82, 80.72, 80.63, 80.53, 80.44, 80.34, 80.15, 79.96, 79.76, 79.57, 79.38, 79.19, 79.0]. The historical wind_power data for the past 178 minutes is: [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.08, 0.08]. Think about how Temperature, Relative Humidity influence wind_power. Please give me a forecast for the next 77 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Temperature, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_131.pkl | external_data/ground_truth_data/ground_truth_data_131.pkl | external_data/context/context_131.pkl | external_data/constraint/constraint_131.pkl |
132 | electricity_prediction-load_variability_limit | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 95 minutes. I need to manage the load variability so that it does not exceed 0.0010715781431239362 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 32 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Relative Humidity, Wind Speed data and the corresponding wind_power data for the past 95 minutes. I need to manage the load variability so that it does not exceed 0.0010715781431239362 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Relative Humidity data for the past 95 minutes is: [85.3, 85.42, 85.53, 85.65, 85.76, 85.76, 85.76, 85.76, 85.76, 85.76, 85.88, 85.99, 86.11, 86.22, 86.34, 86.45, 86.57, 86.68, 86.8, 86.91, 87.03, 87.15, 87.26, 87.38, 87.5, 87.5, 87.5, 87.5, 87.5, 87.5, 87.62, 87.73, 87.85, 87.96, 88.08, 88.2, 88.32, 88.43, 88.55, 88.67, 87.88, 87.09, 86.3, 85.51, 84.72, 84.83, 84.94, 85.06, 85.17, 85.28, 85.4, 85.51, 85.63, 85.74, 85.86, 85.86, 85.86, 85.86, 85.86, 85.86, 85.97, 86.09, 86.2, 86.32, 86.43, 86.55, 86.66, 86.78, 86.89, 87.01, 87.01, 87.01, 87.01, 87.01, 87.01, 87.13, 87.25, 87.36, 87.48, 87.6, 87.72, 87.84, 87.95, 88.07, 88.19, 88.19, 88.19, 88.19, 88.19, 88.19, 88.31, 88.43, 88.55, 88.67, 88.79]. The historical Wind Speed data for the past 95 minutes is: [0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]. The historical wind_power data for the past 95 minutes is: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. Think about how Relative Humidity, Wind Speed influence wind_power. Please give me a forecast for the next 32 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Relative Humidity, Wind Speed are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_132.pkl | external_data/ground_truth_data/ground_truth_data_132.pkl | external_data/context/context_132.pkl | external_data/constraint/constraint_132.pkl |
133 | electricity_prediction-load_variability_limit | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 97 minutes. I need to manage the load variability so that it does not exceed 0.022879226647727943 MW over the complete time period (i.e the maximum change in load over the entire period). Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 70 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have historical Wind Speed, Relative Humidity data and the corresponding wind_power data for the past 97 minutes. I need to manage the load variability so that it does not exceed 0.022879226647727943 MW over the complete time period (i.e the maximum change in load over the entire period). The historical Wind Speed data for the past 97 minutes is: [2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.6, 2.58, 2.56, 2.54, 2.52, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5]. The historical Relative Humidity data for the past 97 minutes is: [41.05, 41.05, 41.05, 41.05, 41.05, 41.05, 41.0, 40.95, 40.91, 40.86, 40.81, 40.76, 40.71, 40.67, 40.62, 40.57, 40.57, 40.57, 40.57, 40.57, 40.57, 40.51, 40.44, 40.38, 40.31, 40.25, 40.25, 40.25, 40.25, 40.25, 40.25, 40.2, 40.16, 40.11, 40.07, 40.02, 39.97, 39.93, 39.88, 39.84, 39.79, 39.79, 39.79, 39.79, 39.79, 39.79, 39.74, 39.7, 39.65, 39.61, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.56, 39.51, 39.47, 39.42, 39.38, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.33, 39.29, 39.25, 39.2, 39.16, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.12, 39.07]. The historical wind_power data for the past 97 minutes is: [0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.07, 0.07, 0.06, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.07]. Think about how Wind Speed, Relative Humidity influence wind_power. Please give me a forecast for the next 70 minutes for wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for both covariates and wind_power are saved in variable VAL with last column being the target variable and future data of Wind Speed, Relative Humidity are saved in variable MULVAL with last column being the target variable. | external_data/executor_variables/executor_variables_133.pkl | external_data/ground_truth_data/ground_truth_data_133.pkl | external_data/context/context_133.pkl | external_data/constraint/constraint_133.pkl |
134 | electricity_prediction_single-max_load | I have load_power data for the past 183 minutes. I need to ensure that the maximum allowable system load does not exceed 715.3699199285553 MW. Please give me a forecast for the next 54 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 183 minutes. I need to ensure that the maximum allowable system load does not exceed 715.3699199285553 MW. The historical load_power data for the past 183 minutes is: [654.94, 654.74, 648.88, 637.3, 624.13, 610.95, 598.12, 592.27, 593.37, 589.41, 589.55, 593.44, 607.07, 616.41, 624.34, 628.14, 633.82, 635.04, 640.94, 649.52, 651.47, 638.42, 633.37, 628.65, 621.0, 617.08, 608.26, 595.5, 583.74, 572.97, 562.23, 557.29, 555.66, 560.3, 564.22, 568.89, 575.51, 582.89, 592.89, 588.97, 593.76, 593.17, 587.69, 597.99, 594.97, 598.35, 598.23, 600.23, 605.65, 607.54, 605.41, 598.59, 582.95, 571.13, 563.51, 557.07, 548.7, 549.6, 549.82, 554.2, 556.26, 565.5, 576.52, 581.07, 582.14, 581.44, 576.81, 572.45, 577.74, 595.71, 606.1, 603.34, 601.74, 601.27, 601.2, 590.8, 579.55, 572.64, 566.64, 559.73, 553.75, 552.51, 557.31, 560.52, 562.35, 567.82, 573.39, 580.44, 582.51, 591.07, 592.88, 599.27, 597.76, 601.02, 606.14, 611.27, 619.25, 618.15, 619.41, 617.49, 607.42, 600.28, 597.05, 593.19, 590.86, 568.34, 586.67, 596.5, 604.45, 612.55, 613.09, 604.45, 615.78, 619.85, 624.09, 572.58, 633.42, 624.12, 623.05, 633.1, 641.0, 642.18, 640.08, 625.66, 612.51, 600.26, 590.0, 583.91, 578.37, 577.71, 580.11, 587.28, 597.26, 607.22, 620.47, 625.03, 626.34, 620.68, 614.7, 618.5, 626.96, 625.81, 636.55, 644.69, 646.87, 647.48, 648.04, 634.46, 616.63, 604.07, 596.96, 591.48, 585.39, 584.52, 593.68, 600.4, 616.46, 621.89, 626.87, 633.94, 638.2, 645.8, 647.33, 640.65, 623.78, 645.9, 644.32, 664.9, 673.54, 660.33, 654.08, 640.72, 619.74, 606.44, 597.57, 592.82, 589.24, 582.09, 584.77, 596.94, 604.85, 616.63, 619.98]. Please give me a forecast for the next 54 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_134.pkl | external_data/ground_truth_data/ground_truth_data_134.pkl | external_data/context/context_134.pkl | external_data/constraint/constraint_134.pkl |
135 | electricity_prediction_single-max_load | I have load_power data for the past 89 minutes. I need to ensure that the maximum allowable system load does not exceed 1789.1751112148352 MW. Please give me a forecast for the next 14 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 89 minutes. I need to ensure that the maximum allowable system load does not exceed 1789.1751112148352 MW. The historical load_power data for the past 89 minutes is: [1422.93, 1385.27, 1373.82, 1355.01, 1345.61, 1365.47, 1434.7, 1509.01, 1595.75, 1651.62, 1674.42, 1678.9, 1666.41, 1647.06, 1658.87, 1652.31, 1662.77, 1698.76, 1731.33, 1766.08, 1796.92, 1757.92, 1690.94, 1577.35, 1512.94, 1460.8, 1431.46, 1409.17, 1392.45, 1416.44, 1475.82, 1602.46, 1720.28, 1746.47, 1762.39, 1771.92, 1767.81, 1760.77, 1740.62, 1732.76, 1728.59, 1749.3, 1770.04, 1807.56, 1816.23, 1737.33, 1655.58, 1564.84, 1502.94, 1437.43, 1400.45, 1379.47, 1372.2, 1389.34, 1452.46, 1586.7, 1679.58, 1695.48, 1689.86, 1690.36, 1673.82, 1689.24, 1701.46, 1701.25, 1693.5, 1710.29, 1733.16, 1757.45, 1774.23, 1739.17, 1669.03, 1576.79, 1493.55, 1443.94, 1411.8, 1394.83, 1382.61, 1402.1, 1466.42, 1593.33, 1685.76, 1704.68, 1670.15, 1585.34, 1582.46, 1596.26, 1596.22, 1590.33, 1567.42]. Please give me a forecast for the next 14 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_135.pkl | external_data/ground_truth_data/ground_truth_data_135.pkl | external_data/context/context_135.pkl | external_data/constraint/constraint_135.pkl |
136 | electricity_prediction_single-max_load | I have load_power data for the past 90 minutes. I need to ensure that the maximum allowable system load does not exceed 21160.819510692007 MW. Please give me a forecast for the next 63 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 90 minutes. I need to ensure that the maximum allowable system load does not exceed 21160.819510692007 MW. The historical load_power data for the past 90 minutes is: [14735.5, 14718.76, 14789.39, 14991.13, 15263.24, 15689.96, 15960.72, 15993.84, 15898.0, 15863.58, 15780.87, 15696.71, 15748.37, 16098.48, 16752.72, 17607.83, 17582.68, 17381.41, 17103.61, 16620.84, 16048.91, 15560.62, 15207.42, 14953.97, 14933.69, 14997.36, 15200.83, 15676.93, 16239.32, 16854.58, 17243.06, 17539.63, 17775.13, 17869.93, 17815.2, 17776.62, 17808.51, 18042.45, 18771.13, 19461.51, 19299.27, 19004.57, 18631.07, 18107.15, 17472.55, 16861.35, 16445.26, 16267.57, 16150.47, 16228.77, 16522.69, 17347.58, 18473.43, 19391.03, 19645.99, 19744.35, 19804.16, 19904.45, 19825.13, 19670.44, 19456.15, 19248.1, 19536.79, 20039.54, 19801.38, 19390.72, 18874.3, 18208.79, 17550.46, 17026.6, 16532.63, 16262.91, 16130.0, 16128.13, 16464.3, 17186.09, 18272.28, 19126.62, 19358.43, 19408.06, 19508.45, 19566.86, 19611.76, 19503.4, 19359.68, 19275.98, 19660.32, 20213.21, 20074.6, 19721.41]. Please give me a forecast for the next 63 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_136.pkl | external_data/ground_truth_data/ground_truth_data_136.pkl | external_data/context/context_136.pkl | external_data/constraint/constraint_136.pkl |
137 | electricity_prediction_single-max_load | I have load_power data for the past 186 minutes. I need to ensure that the maximum allowable system load does not exceed 4327.6684020603 MW. Please give me a forecast for the next 54 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 186 minutes. I need to ensure that the maximum allowable system load does not exceed 4327.6684020603 MW. The historical load_power data for the past 186 minutes is: [3126.2, 3022.93, 3030.57, 3120.69, 3319.84, 3319.46, 3275.63, 3209.55, 3136.8, 3120.42, 3071.89, 3012.14, 3003.76, 3059.23, 3055.08, 3213.53, 3394.97, 3446.13, 3314.81, 3178.82, 3112.49, 3058.07, 3151.6, 3235.89, 3321.1, 3406.92, 3300.82, 3370.11, 3460.61, 3433.37, 3342.9, 3155.15, 2937.88, 2733.49, 2607.43, 2550.69, 2548.65, 2588.4, 2746.71, 3069.08, 3162.3, 3116.65, 3141.47, 3169.16, 3275.92, 3370.61, 3407.41, 3435.92, 3615.03, 3632.21, 3615.21, 3692.51, 3755.03, 3739.42, 3625.77, 3423.56, 3166.79, 2876.26, 2754.18, 2657.89, 2626.92, 2602.1, 2790.12, 3102.22, 3164.48, 3142.04, 3209.77, 3274.59, 3414.2, 3520.46, 3563.83, 3589.06, 3593.35, 3699.45, 3603.58, 3666.73, 3782.22, 3728.25, 3612.57, 3377.52, 3144.97, 2945.36, 2868.72, 2792.05, 2771.43, 2787.56, 2867.69, 3211.65, 3323.54, 3269.65, 3425.77, 3445.63, 3466.34, 3455.63, 3427.61, 3293.06, 3274.83, 3305.13, 3259.13, 3459.65, 3547.95, 3541.89, 3523.19, 3298.08, 3049.7, 2975.27, 2927.29, 2918.21, 2879.81, 2961.03, 3148.59, 3554.55, 3653.65, 3512.17, 3350.59, 3230.19, 3125.48, 3065.54, 3078.22, 3110.72, 3142.57, 3169.05, 3185.99, 3304.05, 3404.88, 3395.0, 3302.47, 3177.01, 2977.15, 2751.41, 2759.3, 2668.79, 2666.68, 2751.59, 2912.8, 3292.84, 3453.47, 3430.1, 3438.04, 3377.35, 3292.28, 3193.83, 3147.1, 3183.96, 3214.66, 3249.07, 3322.05, 3504.48, 3624.05, 3651.39, 3658.5, 3650.07, 3636.83, 3569.09, 3543.32, 3559.16, 3517.07, 3704.68, 3781.07, 4001.95, 4276.71, 4322.83, 4210.36, 3956.58, 3708.22, 3472.26, 3300.48, 3196.93, 3157.13, 3065.06, 3092.97, 3255.97, 3507.5, 3598.27, 3655.78, 3651.24, 3618.09, 3607.81, 3589.67, 3578.4, 3636.77, 3711.77, 3706.9, 3846.73, 4049.03, 4094.05]. Please give me a forecast for the next 54 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_137.pkl | external_data/ground_truth_data/ground_truth_data_137.pkl | external_data/context/context_137.pkl | external_data/constraint/constraint_137.pkl |
138 | electricity_prediction_single-max_load | I have load_power data for the past 81 minutes. I need to ensure that the maximum allowable system load does not exceed 6107.58935968315 MW. Please give me a forecast for the next 82 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 81 minutes. I need to ensure that the maximum allowable system load does not exceed 6107.58935968315 MW. The historical load_power data for the past 81 minutes is: [4027.12, 4183.57, 4323.25, 4449.8, 4554.66, 4643.79, 4686.05, 4682.29, 4660.96, 4695.04, 4736.96, 4831.44, 4883.8, 4825.05, 4710.24, 4524.9, 4294.79, 4087.7, 3920.0, 3810.8, 3746.47, 3754.15, 3909.32, 4253.64, 4593.52, 4860.27, 5014.65, 5075.1, 5123.62, 5162.13, 5184.31, 5204.21, 5285.23, 5364.49, 5383.69, 5366.59, 5342.65, 5176.26, 4985.78, 4703.21, 4395.66, 4147.07, 3962.08, 3862.0, 3808.64, 3826.85, 4037.26, 4506.35, 4936.52, 5193.09, 5349.64, 5372.9, 5401.66, 5510.85, 5411.19, 5435.55, 5523.8, 5641.81, 5614.69, 5538.99, 5457.79, 5267.78, 5052.79, 4754.52, 4424.9, 4157.48, 3983.54, 3879.06, 3819.73, 3846.54, 4040.96, 4525.07, 4958.94, 5210.88, 5355.85, 5407.63, 5466.52, 5522.04, 5557.54, 5585.37, 5650.11]. Please give me a forecast for the next 82 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_138.pkl | external_data/ground_truth_data/ground_truth_data_138.pkl | external_data/context/context_138.pkl | external_data/constraint/constraint_138.pkl |
139 | electricity_prediction_single-max_load | I have load_power data for the past 118 minutes. I need to ensure that the maximum allowable system load does not exceed 1597.8584140811174 MW. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 118 minutes. I need to ensure that the maximum allowable system load does not exceed 1597.8584140811174 MW. The historical load_power data for the past 118 minutes is: [1381.12, 1332.82, 1286.49, 1258.2, 1254.4, 1259.67, 1284.89, 1340.61, 1435.27, 1516.42, 1535.27, 1553.91, 1560.31, 1571.0, 1564.3, 1569.7, 1558.12, 1558.85, 1580.05, 1563.89, 1566.41, 1564.85, 1573.21, 1551.02, 1475.68, 1395.67, 1337.4, 1301.17, 1283.59, 1275.0, 1296.11, 1343.96, 1429.82, 1508.88, 1537.99, 1549.59, 1555.53, 1564.96, 1566.34, 1602.7, 1612.89, 1625.62, 1646.44, 1666.75, 1661.89, 1653.99, 1633.02, 1619.06, 1520.04, 1421.78, 1358.38, 1321.54, 1296.52, 1282.8, 1289.6, 1341.72, 1419.85, 1504.61, 1545.47, 1565.62, 1591.04, 1628.78, 1609.85, 1611.13, 1589.21, 1559.57, 1537.69, 1511.03, 1517.3, 1548.24, 1550.62, 1541.09, 1464.76, 1384.58, 1341.46, 1309.13, 1280.43, 1290.72, 1307.14, 1367.36, 1457.8, 1525.29, 1528.18, 1510.94, 1507.49, 1488.99, 1484.92, 1493.15, 1469.37, 1462.51, 1474.37, 1491.07, 1520.56, 1551.41, 1563.07, 1563.96, 1494.88, 1409.38, 1348.14, 1323.96, 1307.22, 1305.25, 1322.41, 1372.07, 1446.6, 1508.23, 1523.78, 1518.74, 1489.61, 1496.64, 1483.67, 1491.63, 1471.28, 1461.44, 1462.14, 1491.19, 1508.61, 1524.62]. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_139.pkl | external_data/ground_truth_data/ground_truth_data_139.pkl | external_data/context/context_139.pkl | external_data/constraint/constraint_139.pkl |
140 | electricity_prediction_single-max_load | I have load_power data for the past 130 minutes. I need to ensure that the maximum allowable system load does not exceed 21475.230282951885 MW. Please give me a forecast for the next 40 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 130 minutes. I need to ensure that the maximum allowable system load does not exceed 21475.230282951885 MW. The historical load_power data for the past 130 minutes is: [15458.21, 15314.05, 15321.13, 15632.53, 16455.36, 17768.59, 18649.95, 18604.91, 18468.25, 18537.11, 18527.0, 18549.15, 18468.59, 18302.45, 18230.35, 18285.4, 18706.66, 18799.15, 18483.19, 18055.02, 17411.57, 16833.19, 16368.13, 16006.44, 15788.43, 15714.54, 15797.88, 16165.68, 17194.26, 18537.4, 19410.28, 19556.51, 19442.63, 19002.27, 18600.76, 18408.67, 18348.58, 18181.34, 18203.22, 18700.66, 19464.75, 19957.98, 19829.7, 19441.65, 18837.97, 18290.08, 17807.08, 17294.21, 17087.66, 17052.09, 17090.89, 17532.23, 18276.66, 19495.56, 20112.63, 19979.85, 19786.57, 19521.41, 19272.11, 18897.93, 18652.39, 18499.84, 18424.84, 18913.66, 19683.35, 20205.64, 19982.65, 19660.44, 19146.26, 18589.39, 18104.32, 17683.21, 17394.53, 17312.3, 17415.12, 17729.77, 18573.46, 19783.69, 20403.22, 19916.5, 19151.79, 18822.11, 18577.06, 18368.1, 18025.84, 17621.45, 17502.96, 18003.81, 18771.94, 19186.18, 18881.22, 18546.14, 17964.02, 17480.5, 16949.89, 16403.39, 15991.31, 15792.81, 15749.42, 15868.76, 16197.45, 16701.11, 16970.15, 16760.47, 16394.23, 16220.2, 15955.72, 15674.25, 15445.11, 15342.79, 15541.02, 16095.32, 16856.8, 17383.72, 17163.84, 16854.9, 16464.52, 16092.15, 15735.05, 15409.44, 15253.94, 15118.21, 15109.88, 15147.43, 15333.92, 15706.06, 16048.0, 16171.02, 15997.24, 15963.44]. Please give me a forecast for the next 40 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_140.pkl | external_data/ground_truth_data/ground_truth_data_140.pkl | external_data/context/context_140.pkl | external_data/constraint/constraint_140.pkl |
141 | electricity_prediction_single-max_load | I have load_power data for the past 118 minutes. I need to ensure that the maximum allowable system load does not exceed 1314.5646346888948 MW. Please give me a forecast for the next 88 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 118 minutes. I need to ensure that the maximum allowable system load does not exceed 1314.5646346888948 MW. The historical load_power data for the past 118 minutes is: [1016.32, 982.03, 966.33, 955.04, 959.54, 992.84, 1067.82, 1109.34, 1104.28, 1106.12, 1134.29, 1175.74, 1223.67, 1291.21, 1357.68, 1417.93, 1467.77, 1479.7, 1458.35, 1407.81, 1379.2, 1309.52, 1211.18, 1127.88, 1077.74, 1036.82, 1019.45, 1011.0, 1010.49, 1047.16, 1119.95, 1145.38, 1136.8, 1164.72, 1202.71, 1246.92, 1292.93, 1363.56, 1388.29, 1394.3, 1412.24, 1405.6, 1389.5, 1362.83, 1365.9, 1326.66, 1245.64, 1163.15, 1082.41, 1042.17, 1013.49, 995.44, 999.24, 1022.18, 1092.3, 1152.74, 1149.76, 1154.3, 1142.31, 1123.71, 1093.82, 1088.95, 1086.36, 1104.69, 1130.34, 1149.81, 1149.65, 1132.52, 1147.11, 1127.52, 1070.59, 1014.89, 995.72, 971.36, 962.97, 965.79, 976.01, 1026.79, 1121.88, 1168.24, 1171.84, 1166.48, 1138.62, 1127.67, 1111.54, 1105.93, 1110.5, 1094.9, 1110.43, 1118.34, 1108.35, 1109.55, 1158.59, 1159.06, 1125.58, 1080.31, 1016.05, 998.38, 991.68, 986.52, 992.92, 1027.79, 1081.96, 1125.14, 1145.32, 1158.84, 1153.76, 1130.1, 1095.65, 1079.72, 1076.37, 1084.38, 1100.5, 1105.75, 1107.52, 1102.87, 1109.5, 1099.81]. Please give me a forecast for the next 88 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_141.pkl | external_data/ground_truth_data/ground_truth_data_141.pkl | external_data/context/context_141.pkl | external_data/constraint/constraint_141.pkl |
142 | electricity_prediction_single-max_load | I have load_power data for the past 121 minutes. I need to ensure that the maximum allowable system load does not exceed 8410.204509890109 MW. Please give me a forecast for the next 19 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 121 minutes. I need to ensure that the maximum allowable system load does not exceed 8410.204509890109 MW. The historical load_power data for the past 121 minutes is: [8019.21, 8031.47, 8038.93, 8241.36, 8499.34, 8753.4, 9002.59, 9313.6, 9542.0, 9705.3, 9883.66, 9955.54, 9861.73, 9746.33, 9498.19, 9055.55, 8481.55, 7899.49, 7070.94, 6721.63, 6499.78, 6370.2, 6362.28, 6577.18, 7011.58, 7237.85, 7392.56, 7716.26, 8091.76, 8492.8, 8869.9, 9228.78, 9500.47, 9661.82, 9729.28, 9638.17, 9359.74, 9126.27, 8826.99, 8437.19, 8043.1, 7534.29, 6928.13, 6544.49, 6284.31, 6085.87, 5980.85, 5976.3, 6092.08, 6211.79, 6384.07, 6560.34, 6689.96, 6795.24, 6927.49, 7078.53, 7321.17, 7486.56, 7670.59, 7710.93, 7589.75, 7563.64, 7410.14, 7158.01, 6843.48, 6466.04, 5910.41, 5624.63, 5421.47, 5297.67, 5238.82, 5238.98, 5346.63, 5475.01, 5679.61, 5900.61, 6082.64, 6295.78, 6502.4, 6774.02, 7050.19, 7363.37, 7559.31, 7589.38, 7477.87, 7477.08, 7489.24, 7257.63, 6812.35, 6344.93, 5929.2, 5646.2, 5456.39, 5391.92, 5458.52, 5655.77, 6001.98, 6255.94, 6414.75, 6603.74, 6866.47, 7154.18, 7485.86, 7846.31, 8210.46, 8567.82, 8897.82, 9099.7, 8914.64, 8876.15, 8662.11, 8310.77, 7705.97, 7115.68, 6604.26, 6311.05, 6115.57, 6004.59, 6052.45, 6285.14, 6746.04]. Please give me a forecast for the next 19 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_142.pkl | external_data/ground_truth_data/ground_truth_data_142.pkl | external_data/context/context_142.pkl | external_data/constraint/constraint_142.pkl |
143 | electricity_prediction_single-max_load | I have load_power data for the past 72 minutes. I need to ensure that the maximum allowable system load does not exceed 6365.814810998243 MW. Please give me a forecast for the next 82 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 72 minutes. I need to ensure that the maximum allowable system load does not exceed 6365.814810998243 MW. The historical load_power data for the past 72 minutes is: [5179.93, 4948.65, 4769.06, 4639.53, 4570.51, 4595.62, 4704.48, 4925.81, 5178.43, 5418.09, 5616.51, 5763.26, 5826.39, 5853.9, 5913.75, 6001.82, 5997.31, 5922.72, 5828.55, 5727.73, 5693.67, 5589.63, 5384.91, 5106.38, 4802.56, 4543.89, 4332.47, 4197.0, 4105.66, 4065.49, 4101.32, 4243.44, 4416.91, 4584.19, 4725.23, 4830.37, 4880.02, 4929.36, 4985.35, 5062.08, 5154.65, 5209.87, 5211.79, 5135.05, 5156.93, 5083.9, 4882.88, 4604.3, 4344.46, 4138.76, 4002.07, 3934.42, 3952.77, 4126.12, 4521.71, 4951.76, 5257.1, 5453.25, 5541.07, 5606.74, 5688.38, 5742.16, 5809.35, 5910.46, 6023.77, 6056.29, 5860.05, 5687.86, 5560.31, 5385.41, 5088.86, 4745.66]. Please give me a forecast for the next 82 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_143.pkl | external_data/ground_truth_data/ground_truth_data_143.pkl | external_data/context/context_143.pkl | external_data/constraint/constraint_143.pkl |
144 | electricity_prediction_single-max_load | I have load_power data for the past 63 minutes. I need to ensure that the maximum allowable system load does not exceed 1375.2479098603476 MW. Please give me a forecast for the next 26 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 63 minutes. I need to ensure that the maximum allowable system load does not exceed 1375.2479098603476 MW. The historical load_power data for the past 63 minutes is: [1133.86, 1114.76, 1097.26, 1097.63, 1107.48, 1125.36, 1192.89, 1218.57, 1208.38, 1179.48, 1130.86, 1090.72, 1117.35, 1109.54, 1113.22, 1110.63, 1130.94, 1188.1, 1266.18, 1313.13, 1306.9, 1288.8, 1249.32, 1210.32, 1172.67, 1155.53, 1134.02, 1133.9, 1140.47, 1163.24, 1226.73, 1239.42, 1239.21, 1227.88, 1197.78, 1169.04, 1195.02, 1198.34, 1204.45, 1211.15, 1247.74, 1315.74, 1428.87, 1485.27, 1456.35, 1386.52, 1303.0, 1234.35, 1180.32, 1135.42, 1108.3, 1102.04, 1093.88, 1119.9, 1197.4, 1212.89, 1232.22, 1223.91, 1198.56, 1169.12, 1107.83, 1113.39, 1119.12]. Please give me a forecast for the next 26 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_144.pkl | external_data/ground_truth_data/ground_truth_data_144.pkl | external_data/context/context_144.pkl | external_data/constraint/constraint_144.pkl |
145 | electricity_prediction_single-max_load | I have load_power data for the past 85 minutes. I need to ensure that the maximum allowable system load does not exceed 23600.04383785813 MW. Please give me a forecast for the next 53 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 85 minutes. I need to ensure that the maximum allowable system load does not exceed 23600.04383785813 MW. The historical load_power data for the past 85 minutes is: [19910.8, 19486.84, 19476.68, 18682.05, 17563.82, 16234.16, 15469.68, 14970.79, 14725.82, 14646.43, 14717.2, 15339.39, 16437.84, 16746.05, 16927.33, 16985.97, 17376.51, 17692.52, 18014.87, 18740.6, 19309.38, 19743.45, 20028.47, 20048.99, 19736.2, 19372.72, 19472.66, 18728.02, 17564.61, 16348.32, 15630.23, 15131.52, 14852.43, 14583.33, 14805.35, 15596.59, 16443.77, 16721.99, 16880.67, 17084.22, 17754.1, 18269.35, 18722.99, 19480.02, 20263.24, 20918.07, 21355.62, 21439.84, 21098.4, 20450.8, 20188.82, 19366.78, 17962.73, 16636.66, 15718.64, 15075.13, 14841.64, 14485.43, 14443.91, 15125.03, 16194.16, 16565.65, 16679.86, 17121.47, 17754.11, 18470.96, 19311.18, 20283.32, 20791.34, 21416.89, 21546.73, 21379.9, 20874.78, 20495.06, 20613.15, 19917.55, 18580.64, 17472.88, 16722.71, 16268.0, 15900.56, 15635.51, 15729.58, 16239.08, 17308.05]. Please give me a forecast for the next 53 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_145.pkl | external_data/ground_truth_data/ground_truth_data_145.pkl | external_data/context/context_145.pkl | external_data/constraint/constraint_145.pkl |
146 | electricity_prediction_single-max_load | I have load_power data for the past 196 minutes. I need to ensure that the maximum allowable system load does not exceed 1167.4477545145928 MW. Please give me a forecast for the next 57 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 196 minutes. I need to ensure that the maximum allowable system load does not exceed 1167.4477545145928 MW. The historical load_power data for the past 196 minutes is: [778.83, 767.59, 774.09, 804.25, 848.8, 884.2, 914.21, 932.54, 950.37, 961.03, 953.73, 947.64, 944.74, 951.63, 981.29, 1027.53, 1035.74, 1000.7, 957.96, 903.91, 850.29, 797.78, 760.55, 750.88, 748.65, 756.67, 781.72, 843.41, 948.7, 998.36, 995.6, 991.37, 990.94, 992.73, 992.27, 992.2, 978.03, 980.5, 1012.28, 1056.01, 1068.91, 1057.22, 1015.35, 965.67, 905.67, 861.73, 830.12, 815.03, 810.3, 810.73, 827.74, 893.57, 990.41, 1040.54, 1057.92, 1067.86, 1058.22, 1028.73, 1003.95, 1004.5, 1011.78, 1028.86, 1069.05, 1135.61, 1154.27, 1163.6, 1132.42, 1086.65, 1029.33, 981.62, 944.23, 936.66, 921.21, 921.39, 940.94, 1016.65, 1127.84, 1168.76, 1147.88, 1108.8, 1070.41, 1048.16, 1022.16, 992.0, 990.43, 1017.69, 1085.28, 1156.94, 1188.28, 1175.93, 1140.89, 1086.27, 1033.75, 978.21, 951.94, 936.07, 932.97, 935.09, 954.84, 1011.29, 1109.35, 1157.74, 1136.98, 1115.27, 1065.86, 1024.02, 992.73, 975.48, 971.99, 995.12, 1061.22, 1137.64, 1159.5, 1132.42, 1095.63, 1046.61, 986.49, 943.64, 931.59, 911.87, 909.52, 920.45, 943.11, 992.91, 1105.62, 1165.64, 1151.94, 1121.56, 1114.73, 1109.71, 1092.16, 1116.86, 1147.31, 1192.53, 1254.97, 1330.42, 1344.11, 1327.71, 1300.88, 1262.85, 1225.98, 1170.16, 1141.94, 1120.38, 1118.32, 1112.74, 1131.06, 1157.06, 1197.65, 1233.86, 1232.14, 1201.04, 1178.39, 1157.31, 1167.42, 1167.05, 1164.74, 1189.33, 1220.91, 1275.68, 1282.61, 1256.28, 1229.8, 1176.69, 1123.21, 1066.09, 1011.66, 974.4, 966.85, 962.78, 961.0, 971.43, 1003.44, 1031.23, 1045.16, 1038.5, 1008.4, 950.22, 891.96, 879.99, 912.76, 945.66, 989.12, 1037.42, 1046.18, 1029.11, 987.73, 935.11, 882.29, 836.35, 794.36, 777.44, 777.31, 778.7, 801.09, 864.0]. Please give me a forecast for the next 57 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_146.pkl | external_data/ground_truth_data/ground_truth_data_146.pkl | external_data/context/context_146.pkl | external_data/constraint/constraint_146.pkl |
147 | electricity_prediction_single-max_load | I have load_power data for the past 180 minutes. I need to ensure that the maximum allowable system load does not exceed 25825.174222682366 MW. Please give me a forecast for the next 41 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 180 minutes. I need to ensure that the maximum allowable system load does not exceed 25825.174222682366 MW. The historical load_power data for the past 180 minutes is: [11207.78, 11703.04, 12665.88, 13109.59, 13308.96, 13580.13, 13769.89, 13972.92, 14196.14, 14491.42, 14800.69, 15130.51, 15362.38, 15449.42, 15383.29, 15429.82, 15452.02, 14834.43, 13869.77, 12745.78, 11947.71, 11367.17, 11052.37, 10938.27, 11058.81, 11533.17, 12394.97, 12831.32, 13098.76, 13586.63, 13936.71, 14191.47, 14476.39, 15069.69, 15785.29, 16540.4, 17046.25, 17334.07, 17207.52, 16568.69, 16105.66, 15255.8, 14289.02, 13256.85, 12350.36, 11657.91, 11120.2, 10746.55, 10550.62, 10670.62, 10915.72, 11204.04, 11937.01, 12707.91, 13599.97, 14068.29, 14461.88, 14839.05, 15584.33, 16648.2, 17212.5, 17186.61, 16845.87, 16422.92, 15969.52, 15140.5, 14175.09, 13291.96, 12368.64, 11674.42, 11159.38, 10715.7, 10484.97, 10390.8, 10444.08, 10570.49, 11233.15, 12254.02, 13383.98, 14618.81, 15893.12, 17009.7, 18063.25, 18987.79, 19573.91, 19734.94, 19057.38, 18032.91, 17107.81, 15928.34, 14489.26, 13049.49, 11917.51, 11139.39, 10557.04, 10379.76, 10391.59, 10912.73, 11839.66, 12125.8, 12699.83, 13596.12, 14876.5, 16166.13, 17359.91, 18505.34, 19541.31, 20341.46, 20949.76, 21145.3, 20544.93, 19419.82, 18506.8, 17185.93, 15716.04, 14284.97, 13165.85, 12390.45, 11824.9, 11496.93, 11613.67, 12084.2, 12952.97, 13311.61, 13751.15, 14772.35, 15975.85, 17209.03, 18441.6, 19737.12, 20679.1, 21287.4, 21728.74, 21650.34, 20857.21, 19635.24, 18882.39, 17911.9, 16607.74, 14843.88, 13342.01, 12535.17, 11993.41, 11605.41, 11619.29, 12084.16, 13059.31, 13475.64, 13927.87, 14983.73, 16125.28, 17066.69, 18110.35, 19317.36, 20296.06, 21150.33, 21835.05, 22049.13, 21328.81, 20419.61, 19944.77, 18943.07, 17398.77, 15936.24, 14856.54, 13947.27, 13317.09, 12892.86, 12861.72, 13179.19, 13954.56, 14296.64, 14643.0, 15508.6, 16718.25, 17880.83, 19012.21, 20183.71, 21239.2, 22234.08]. Please give me a forecast for the next 41 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_147.pkl | external_data/ground_truth_data/ground_truth_data_147.pkl | external_data/context/context_147.pkl | external_data/constraint/constraint_147.pkl |
148 | electricity_prediction_single-max_load | I have load_power data for the past 197 minutes. I need to ensure that the maximum allowable system load does not exceed 1895.6843679088686 MW. Please give me a forecast for the next 89 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 197 minutes. I need to ensure that the maximum allowable system load does not exceed 1895.6843679088686 MW. The historical load_power data for the past 197 minutes is: [1901.26, 1862.21, 1811.74, 1743.53, 1654.74, 1586.29, 1548.85, 1514.89, 1491.06, 1479.72, 1492.17, 1552.02, 1670.18, 1751.09, 1782.02, 1787.16, 1822.51, 1831.09, 1809.58, 1821.56, 1779.57, 1754.78, 1788.16, 1853.57, 1857.86, 1829.97, 1781.78, 1730.9, 1662.23, 1575.7, 1512.46, 1477.13, 1475.8, 1470.71, 1495.69, 1548.13, 1637.83, 1716.83, 1739.62, 1730.53, 1713.2, 1712.54, 1749.06, 1747.67, 1723.55, 1711.69, 1738.84, 1805.08, 1800.44, 1772.51, 1729.66, 1677.13, 1612.92, 1541.55, 1481.51, 1453.93, 1434.21, 1425.12, 1432.8, 1460.99, 1509.31, 1563.94, 1594.8, 1601.56, 1579.49, 1571.16, 1584.79, 1591.53, 1589.06, 1614.44, 1671.05, 1745.7, 1742.76, 1725.54, 1699.17, 1663.22, 1607.44, 1547.89, 1502.79, 1471.98, 1455.55, 1449.91, 1456.11, 1475.64, 1516.63, 1554.99, 1548.73, 1511.45, 1502.08, 1499.17, 1489.61, 1481.49, 1492.75, 1556.38, 1648.37, 1752.21, 1767.24, 1752.95, 1718.09, 1689.09, 1619.93, 1563.96, 1537.21, 1524.84, 1508.92, 1515.03, 1542.83, 1607.96, 1734.15, 1808.93, 1838.87, 1823.4, 1771.8, 1774.61, 1758.67, 1742.68, 1711.23, 1735.72, 1798.03, 1882.51, 1885.36, 1857.85, 1808.48, 1755.46, 1693.21, 1623.38, 1555.72, 1534.88, 1519.1, 1511.33, 1525.59, 1595.07, 1716.4, 1776.62, 1802.24, 1822.6, 1831.91, 1781.76, 1757.49, 1729.57, 1705.32, 1677.61, 1793.12, 1873.01, 1878.88, 1866.21, 1834.61, 1777.08, 1698.17, 1626.68, 1570.72, 1540.83, 1517.32, 1513.29, 1533.14, 1606.15, 1745.64, 1803.95, 1791.79, 1743.02, 1724.5, 1693.83, 1688.4, 1704.69, 1688.67, 1714.6, 1773.91, 1864.71, 1863.95, 1832.52, 1791.66, 1722.76, 1646.76, 1570.81, 1536.0, 1502.95, 1489.34, 1502.25, 1532.04, 1606.21, 1723.66, 1787.19, 1739.35, 1710.63, 1705.03, 1695.15, 1691.74, 1713.39, 1707.15, 1708.92, 1760.76, 1855.88, 1837.21, 1834.26, 1785.47, 1735.79, 1651.45]. Please give me a forecast for the next 89 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_148.pkl | external_data/ground_truth_data/ground_truth_data_148.pkl | external_data/context/context_148.pkl | external_data/constraint/constraint_148.pkl |
149 | electricity_prediction_single-max_load | I have load_power data for the past 84 minutes. I need to ensure that the maximum allowable system load does not exceed 28248.111437947762 MW. Please give me a forecast for the next 85 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 84 minutes. I need to ensure that the maximum allowable system load does not exceed 28248.111437947762 MW. The historical load_power data for the past 84 minutes is: [15570.79, 15460.59, 15716.71, 16052.39, 16017.09, 16105.07, 16275.74, 16680.93, 17288.09, 18027.97, 19098.53, 20433.27, 22080.76, 23386.08, 23723.07, 23643.99, 22891.3, 21919.49, 21388.2, 20186.87, 18712.57, 17571.06, 16471.64, 15761.02, 15437.97, 15203.76, 15470.52, 15677.56, 15987.15, 16941.17, 18286.27, 19729.76, 21093.46, 22546.1, 24028.75, 25084.99, 25571.2, 26102.66, 26344.76, 26159.24, 25406.78, 24301.64, 23723.47, 22271.48, 20729.06, 19284.86, 18290.32, 17532.08, 16921.47, 16534.81, 16454.72, 16657.89, 16798.95, 17291.48, 17959.83, 18855.56, 19985.05, 21324.74, 22860.45, 24214.98, 25177.3, 25826.92, 26081.29, 25938.06, 25358.67, 24301.98, 23415.74, 22051.3, 20566.62, 19160.76, 18173.04, 17385.15, 16804.53, 16485.12, 16396.83, 16524.08, 16677.92, 17416.85, 18585.07, 19819.02, 21040.9, 22202.59, 23542.55, 24707.57]. Please give me a forecast for the next 85 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_149.pkl | external_data/ground_truth_data/ground_truth_data_149.pkl | external_data/context/context_149.pkl | external_data/constraint/constraint_149.pkl |
150 | electricity_prediction_single-max_load | I have load_power data for the past 192 minutes. I need to ensure that the maximum allowable system load does not exceed 648.6911782850908 MW. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 192 minutes. I need to ensure that the maximum allowable system load does not exceed 648.6911782850908 MW. The historical load_power data for the past 192 minutes is: [553.73, 558.07, 570.37, 571.67, 564.15, 570.31, 589.51, 585.95, 590.24, 574.24, 580.2, 583.46, 588.15, 589.11, 605.93, 607.88, 616.3, 621.24, 612.87, 594.92, 587.21, 577.5, 572.41, 557.7, 556.26, 570.65, 572.87, 592.74, 614.16, 611.13, 599.18, 594.09, 592.97, 596.93, 581.1, 577.7, 581.52, 608.08, 612.28, 607.69, 616.95, 625.58, 628.29, 595.19, 598.13, 593.29, 588.45, 586.58, 588.64, 598.17, 609.4, 637.05, 639.01, 627.58, 610.15, 612.16, 597.81, 605.62, 602.96, 599.46, 601.31, 612.31, 619.72, 629.98, 639.96, 643.31, 637.3, 617.1, 591.06, 606.11, 597.45, 603.04, 588.07, 591.2, 612.03, 635.69, 642.6, 625.47, 612.97, 610.25, 605.37, 604.63, 601.83, 600.23, 605.03, 615.0, 632.23, 628.09, 630.18, 632.91, 642.23, 616.84, 604.29, 604.72, 596.35, 599.12, 600.37, 605.34, 598.56, 618.87, 629.24, 624.69, 620.36, 619.03, 613.29, 614.48, 615.68, 613.74, 619.16, 626.39, 637.06, 637.76, 641.43, 642.79, 635.04, 619.73, 611.73, 595.52, 597.82, 598.0, 584.83, 579.0, 599.86, 620.42, 619.68, 622.65, 617.93, 606.04, 614.12, 612.67, 623.58, 616.46, 627.77, 620.08, 622.01, 625.74, 631.79, 635.6, 637.72, 616.39, 596.43, 584.99, 588.93, 585.32, 580.76, 565.21, 567.03, 575.65, 583.16, 589.64, 584.47, 583.43, 587.09, 592.45, 589.87, 590.65, 588.26, 608.0, 619.11, 620.75, 617.9, 626.73, 608.85, 594.08, 595.19, 594.99, 591.56, 586.27, 586.05, 588.3, 578.29, 582.95, 587.3, 591.31, 590.55, 605.09, 591.02, 593.9, 590.51, 593.59, 591.67, 603.45, 611.62, 622.22, 618.33, 622.62, 617.8, 601.02, 604.22, 596.65, 590.98, 588.27]. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_150.pkl | external_data/ground_truth_data/ground_truth_data_150.pkl | external_data/context/context_150.pkl | external_data/constraint/constraint_150.pkl |
151 | electricity_prediction_single-min_load | I have load_power data for the past 69 minutes. I require that the system load is maintained above a minimum of 9025.591823250057 MW. Please give me a forecast for the next 51 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 69 minutes. I require that the system load is maintained above a minimum of 9025.591823250057 MW. The historical load_power data for the past 69 minutes is: [11194.36, 11887.94, 12015.94, 12133.7, 12216.34, 12211.98, 12134.2, 12018.77, 11937.76, 11920.23, 12151.57, 12596.2, 13384.61, 13795.98, 13796.49, 13522.25, 12785.25, 11951.95, 11261.2, 10882.96, 10790.16, 10898.64, 11239.68, 12031.37, 13477.12, 14246.25, 14508.22, 14715.79, 14675.1, 14445.3, 14214.45, 13935.09, 13751.95, 13829.7, 14112.92, 14324.58, 14617.02, 14563.01, 14318.5, 13913.98, 13335.55, 12741.39, 12589.72, 12287.35, 12210.95, 12275.96, 12373.76, 12916.4, 13424.85, 14073.61, 14676.0, 15036.28, 15038.2, 14898.77, 14425.38, 13988.5, 13516.66, 13141.78, 13032.39, 13159.33, 13513.82, 13529.05, 13316.84, 12967.17, 12386.23, 11904.69, 11348.97, 10996.21, 10807.77]. Please give me a forecast for the next 51 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_151.pkl | external_data/ground_truth_data/ground_truth_data_151.pkl | external_data/context/context_151.pkl | external_data/constraint/constraint_151.pkl |
152 | electricity_prediction_single-min_load | I have load_power data for the past 66 minutes. I require that the system load is maintained above a minimum of 848.2442189769447 MW. Please give me a forecast for the next 11 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 66 minutes. I require that the system load is maintained above a minimum of 848.2442189769447 MW. The historical load_power data for the past 66 minutes is: [987.69, 970.48, 954.08, 1028.54, 1090.68, 1135.77, 1165.15, 1167.54, 1157.36, 1155.88, 1168.2, 1177.78, 1186.06, 1196.07, 1226.69, 1265.24, 1308.97, 1319.81, 1290.65, 1240.71, 1176.23, 1093.77, 1022.41, 975.35, 940.61, 944.06, 966.87, 1031.59, 1073.99, 1091.1, 1092.49, 1080.33, 1055.59, 1029.12, 1028.94, 1031.11, 1042.73, 1067.68, 1113.61, 1163.03, 1213.77, 1181.01, 1094.01, 997.81, 919.9, 873.03, 839.56, 827.39, 828.55, 832.05, 859.51, 909.67, 946.3, 944.44, 931.02, 892.46, 861.68, 848.78, 842.4, 844.96, 848.31, 870.52, 927.72, 1029.9, 1060.98, 1025.3]. Please give me a forecast for the next 11 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_152.pkl | external_data/ground_truth_data/ground_truth_data_152.pkl | external_data/context/context_152.pkl | external_data/constraint/constraint_152.pkl |
153 | electricity_prediction_single-min_load | I have load_power data for the past 194 minutes. I require that the system load is maintained above a minimum of 957.5767925839074 MW. Please give me a forecast for the next 47 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 194 minutes. I require that the system load is maintained above a minimum of 957.5767925839074 MW. The historical load_power data for the past 194 minutes is: [1121.82, 1139.07, 1182.21, 1175.71, 1141.84, 1102.15, 1025.09, 1009.91, 1009.03, 1009.61, 1023.06, 1058.68, 1124.94, 1170.81, 1167.27, 1164.0, 1142.43, 1122.23, 1096.81, 1086.88, 1092.49, 1098.46, 1115.61, 1126.23, 1137.3, 1142.89, 1164.12, 1143.0, 1096.53, 1038.84, 1014.18, 999.39, 988.44, 989.24, 992.28, 1015.77, 1075.87, 1117.69, 1115.6, 1121.76, 1121.19, 1120.66, 1128.09, 1161.89, 1187.75, 1214.3, 1226.47, 1216.75, 1196.41, 1193.01, 1212.04, 1190.07, 1145.11, 1099.25, 1071.08, 1046.91, 1041.16, 1043.09, 1061.04, 1111.17, 1199.13, 1266.39, 1280.52, 1286.84, 1290.31, 1258.0, 1216.43, 1188.96, 1154.11, 1135.14, 1131.45, 1131.64, 1150.37, 1178.14, 1205.52, 1203.64, 1175.28, 1147.34, 1128.99, 1139.92, 1141.74, 1143.83, 1154.65, 1184.21, 1226.6, 1274.11, 1300.68, 1306.2, 1293.28, 1268.74, 1243.92, 1222.98, 1203.94, 1190.9, 1190.81, 1196.06, 1194.6, 1207.57, 1230.93, 1227.49, 1209.47, 1177.56, 1225.99, 1218.48, 1216.39, 1218.33, 1229.56, 1250.31, 1294.29, 1346.23, 1376.98, 1355.81, 1298.65, 1222.58, 1151.89, 1107.92, 1080.78, 1065.0, 1066.57, 1084.02, 1089.71, 1134.71, 1211.85, 1212.1, 1179.01, 1139.29, 1104.97, 1089.0, 1098.32, 1108.37, 1137.43, 1203.45, 1310.36, 1369.88, 1348.96, 1333.01, 1324.73, 1325.56, 1308.33, 1269.91, 1247.24, 1235.34, 1235.37, 1249.95, 1258.02, 1283.01, 1312.31, 1286.9, 1229.91, 1173.92, 1137.49, 1114.85, 1100.34, 1096.61, 1107.33, 1144.94, 1196.28, 1220.16, 1184.67, 1167.33, 1143.08, 1111.6, 1098.02, 1094.61, 1087.42, 1095.2, 1116.55, 1139.01, 1145.84, 1149.12, 1180.39, 1171.82, 1117.43, 1053.95, 1008.6, 986.62, 971.98, 965.63, 971.68, 1001.56, 1068.72, 1100.85, 1090.79, 1101.67, 1107.33, 1123.57, 1139.78, 1173.1, 1213.95, 1253.8, 1305.55, 1335.37, 1336.86, 1300.3]. Please give me a forecast for the next 47 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_153.pkl | external_data/ground_truth_data/ground_truth_data_153.pkl | external_data/context/context_153.pkl | external_data/constraint/constraint_153.pkl |
154 | electricity_prediction_single-min_load | I have load_power data for the past 110 minutes. I require that the system load is maintained above a minimum of 258.78290671250835 MW. Please give me a forecast for the next 12 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 110 minutes. I require that the system load is maintained above a minimum of 258.78290671250835 MW. The historical load_power data for the past 110 minutes is: [302.98, 309.78, 317.27, 318.83, 327.41, 350.48, 374.18, 384.22, 370.51, 359.23, 341.84, 316.1, 287.79, 268.1, 259.08, 255.59, 255.35, 262.37, 279.25, 322.97, 340.04, 332.03, 326.64, 320.87, 319.7, 327.55, 328.39, 328.95, 345.04, 371.77, 395.88, 397.89, 387.29, 373.31, 356.23, 326.08, 295.47, 274.85, 269.29, 271.4, 271.81, 282.16, 299.8, 337.4, 349.41, 346.45, 338.6, 334.47, 325.83, 334.87, 359.43, 363.18, 376.99, 400.61, 424.88, 428.26, 420.16, 404.35, 387.64, 361.85, 344.42, 336.72, 329.46, 328.86, 326.2, 330.98, 333.93, 362.72, 378.82, 383.7, 380.3, 375.18, 377.12, 376.32, 378.42, 377.45, 382.75, 386.14, 405.36, 408.49, 401.9, 386.58, 369.08, 323.25, 296.88, 275.92, 264.48, 271.33, 273.98, 281.21, 302.35, 339.52, 356.5, 346.0, 332.43, 320.24, 316.07, 309.7, 301.4, 309.56, 320.31, 341.99, 366.03, 373.15, 365.75, 355.08, 343.18, 319.65, 294.03, 282.75]. Please give me a forecast for the next 12 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_154.pkl | external_data/ground_truth_data/ground_truth_data_154.pkl | external_data/context/context_154.pkl | external_data/constraint/constraint_154.pkl |
155 | electricity_prediction_single-min_load | I have load_power data for the past 57 minutes. I require that the system load is maintained above a minimum of 13445.755257878327 MW. Please give me a forecast for the next 42 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 57 minutes. I require that the system load is maintained above a minimum of 13445.755257878327 MW. The historical load_power data for the past 57 minutes is: [17970.94, 17924.09, 17871.88, 17871.39, 17969.1, 17973.05, 18026.82, 17697.18, 16906.5, 16002.31, 15122.3, 14462.43, 14053.13, 13859.0, 13786.7, 14000.69, 14684.15, 16030.4, 16978.04, 17180.64, 17313.59, 17412.8, 17514.98, 17569.83, 17776.54, 17860.87, 17910.33, 18084.65, 18292.73, 18268.43, 18321.69, 17931.6, 17140.69, 16286.57, 15379.87, 14702.11, 14365.94, 14125.66, 14019.29, 14157.65, 14902.96, 16262.06, 17209.8, 17390.35, 17607.01, 17844.09, 17951.45, 18084.62, 18334.72, 18281.91, 18279.95, 18371.3, 18483.54, 18504.04, 18656.21, 18270.66, 17574.45]. Please give me a forecast for the next 42 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_155.pkl | external_data/ground_truth_data/ground_truth_data_155.pkl | external_data/context/context_155.pkl | external_data/constraint/constraint_155.pkl |
156 | electricity_prediction_single-min_load | I have load_power data for the past 81 minutes. I require that the system load is maintained above a minimum of 247.50048365759284 MW. Please give me a forecast for the next 15 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 81 minutes. I require that the system load is maintained above a minimum of 247.50048365759284 MW. The historical load_power data for the past 81 minutes is: [307.12, 318.49, 327.52, 340.71, 332.64, 318.42, 293.63, 259.3, 249.01, 239.38, 239.09, 236.82, 246.47, 266.89, 297.05, 313.87, 318.52, 327.18, 327.44, 315.1, 295.76, 285.69, 283.12, 283.81, 289.14, 303.99, 318.01, 325.99, 329.11, 324.74, 311.49, 285.23, 263.52, 255.5, 250.65, 247.01, 253.04, 257.51, 270.92, 294.87, 319.34, 336.13, 345.52, 353.75, 353.32, 346.48, 348.82, 350.46, 348.54, 350.18, 350.04, 348.83, 338.7, 325.3, 305.87, 283.58, 262.76, 251.05, 242.69, 241.05, 240.67, 246.29, 258.73, 269.73, 270.15, 263.67, 253.48, 245.39, 235.33, 229.57, 230.78, 232.29, 249.11, 266.21, 285.45, 303.38, 304.24, 293.52, 275.29, 249.8, 248.14]. Please give me a forecast for the next 15 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_156.pkl | external_data/ground_truth_data/ground_truth_data_156.pkl | external_data/context/context_156.pkl | external_data/constraint/constraint_156.pkl |
157 | electricity_prediction_single-min_load | I have load_power data for the past 154 minutes. I require that the system load is maintained above a minimum of 180.24721966413136 MW. Please give me a forecast for the next 43 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 154 minutes. I require that the system load is maintained above a minimum of 180.24721966413136 MW. The historical load_power data for the past 154 minutes is: [402.56, 412.15, 424.45, 440.5, 435.46, 436.26, 431.72, 406.8, 400.44, 402.87, 361.29, 328.41, 301.56, 287.82, 269.41, 250.25, 246.55, 261.64, 289.54, 303.35, 313.58, 308.55, 311.78, 324.39, 356.61, 386.2, 404.08, 433.67, 443.72, 445.9, 431.17, 414.42, 393.38, 369.76, 338.57, 311.88, 281.23, 262.41, 251.03, 241.1, 246.57, 262.95, 283.42, 302.09, 313.14, 315.58, 324.59, 326.51, 336.92, 357.0, 352.52, 352.53, 372.44, 392.67, 398.14, 393.16, 385.13, 359.26, 319.14, 282.92, 262.68, 242.4, 229.46, 220.53, 219.03, 225.78, 240.31, 251.52, 266.02, 271.59, 279.77, 284.14, 292.8, 295.37, 302.78, 313.82, 322.79, 325.38, 319.39, 317.23, 313.83, 293.77, 265.35, 239.22, 221.03, 206.53, 200.1, 193.9, 197.11, 208.14, 229.5, 246.36, 262.59, 257.46, 260.82, 249.39, 244.19, 250.37, 264.6, 273.42, 278.63, 286.87, 290.32, 293.44, 285.46, 272.1, 256.44, 236.69, 222.69, 212.19, 207.98, 203.0, 202.27, 204.83, 201.95, 218.41, 238.82, 247.68, 252.23, 250.5, 246.28, 238.44, 240.09, 249.96, 264.04, 275.11, 278.91, 284.46, 276.68, 266.4, 245.61, 223.12, 208.56, 201.12, 198.01, 189.36, 188.38, 188.44, 207.71, 220.18, 235.84, 238.41, 236.27, 237.76, 242.72, 252.31, 264.31, 286.95, 293.94, 304.27, 306.95, 312.18, 304.98, 291.75]. Please give me a forecast for the next 43 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_157.pkl | external_data/ground_truth_data/ground_truth_data_157.pkl | external_data/context/context_157.pkl | external_data/constraint/constraint_157.pkl |
158 | electricity_prediction_single-min_load | I have load_power data for the past 163 minutes. I require that the system load is maintained above a minimum of 3650.0279664209415 MW. Please give me a forecast for the next 48 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 163 minutes. I require that the system load is maintained above a minimum of 3650.0279664209415 MW. The historical load_power data for the past 163 minutes is: [4586.74, 4348.35, 4161.62, 4053.13, 3997.57, 4023.79, 4232.18, 4656.65, 5056.73, 5340.28, 5527.98, 5588.41, 5645.78, 5668.39, 5666.8, 5651.86, 5676.84, 5729.01, 5814.61, 5691.14, 5529.61, 5349.61, 5159.54, 4925.56, 4661.31, 4424.61, 4243.33, 4115.47, 4050.09, 4042.83, 4108.06, 4260.33, 4434.27, 4603.71, 4732.05, 4827.99, 4873.34, 4906.03, 4911.67, 4928.36, 4975.07, 5077.25, 5241.84, 5245.72, 5177.69, 5079.47, 4959.36, 4805.85, 4599.79, 4407.91, 4241.73, 4131.74, 4062.81, 4048.65, 4114.03, 4232.99, 4373.66, 4527.85, 4684.47, 4809.7, 4896.31, 4943.58, 4950.68, 4982.44, 5058.94, 5198.98, 5379.05, 5402.12, 5352.65, 5260.26, 5120.59, 4890.73, 4630.82, 4427.18, 4277.6, 4176.28, 4141.69, 4196.19, 4443.45, 4915.93, 5324.84, 5545.87, 5663.2, 5682.8, 5699.19, 5725.75, 5713.65, 5737.53, 5833.15, 5958.94, 6060.31, 5928.59, 5774.59, 5585.68, 5361.92, 5047.97, 4734.34, 4477.87, 4267.32, 4155.19, 4101.14, 4145.09, 4378.87, 4855.0, 5275.86, 5475.53, 5585.13, 5616.11, 5621.85, 5643.39, 5657.05, 5671.18, 5744.97, 5871.59, 5994.14, 5886.95, 5737.02, 5555.3, 5343.12, 5027.25, 4699.81, 4441.01, 4263.53, 4164.21, 4113.87, 4164.88, 4397.28, 4881.89, 5292.22, 5528.18, 5633.97, 5625.18, 5675.8, 5683.62, 5714.17, 5715.85, 5753.48, 5886.81, 5995.45, 5861.04, 5714.2, 5525.23, 5305.01, 4990.29, 4657.95, 4394.96, 4217.27, 4111.19, 4060.58, 4102.69, 4325.6, 4798.37, 5204.09, 5427.15, 5538.23, 5565.89, 5580.77, 5591.94, 5607.09, 5624.31, 5679.45, 5791.34, 5885.3]. Please give me a forecast for the next 48 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_158.pkl | external_data/ground_truth_data/ground_truth_data_158.pkl | external_data/context/context_158.pkl | external_data/constraint/constraint_158.pkl |
159 | electricity_prediction_single-min_load | I have load_power data for the past 68 minutes. I require that the system load is maintained above a minimum of 508.51364472338895 MW. Please give me a forecast for the next 74 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 68 minutes. I require that the system load is maintained above a minimum of 508.51364472338895 MW. The historical load_power data for the past 68 minutes is: [777.08, 706.45, 630.07, 598.28, 581.81, 574.84, 577.49, 596.53, 646.99, 748.05, 802.18, 739.07, 655.74, 598.96, 574.93, 565.07, 557.28, 565.68, 580.33, 624.38, 720.71, 804.59, 852.56, 829.56, 766.59, 690.15, 626.12, 598.84, 580.8, 571.23, 580.79, 599.69, 647.9, 749.27, 782.08, 740.57, 673.56, 626.48, 592.05, 595.68, 605.72, 616.16, 656.35, 735.82, 810.89, 859.79, 879.77, 857.85, 803.17, 730.84, 658.63, 627.5, 609.29, 598.58, 600.09, 616.31, 642.66, 740.27, 803.94, 821.88, 818.33, 795.16, 760.93, 730.32, 703.58, 682.3, 688.09, 703.51]. Please give me a forecast for the next 74 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_159.pkl | external_data/ground_truth_data/ground_truth_data_159.pkl | external_data/context/context_159.pkl | external_data/constraint/constraint_159.pkl |
160 | electricity_prediction_single-min_load | I have load_power data for the past 144 minutes. I require that the system load is maintained above a minimum of 2606.526576858313 MW. Please give me a forecast for the next 87 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 144 minutes. I require that the system load is maintained above a minimum of 2606.526576858313 MW. The historical load_power data for the past 144 minutes is: [3270.65, 3423.06, 3389.88, 3371.61, 3420.38, 3370.03, 3336.31, 3312.87, 3300.85, 3446.05, 3523.61, 3400.41, 3210.91, 3059.86, 2919.85, 2742.11, 2785.85, 2763.71, 2791.92, 2929.71, 3030.13, 3100.15, 3234.66, 3373.8, 3536.21, 3689.98, 3839.16, 3963.13, 3969.52, 3940.2, 3892.78, 3795.98, 3711.22, 3847.84, 3820.12, 3632.61, 3340.65, 3097.16, 2931.22, 2731.55, 2658.86, 2656.74, 2759.98, 3039.59, 3135.25, 3092.57, 3146.85, 3229.01, 3353.89, 3476.71, 3679.45, 3715.97, 3817.11, 3927.43, 3915.52, 3852.21, 3739.62, 3867.41, 3784.05, 3515.36, 3262.31, 3046.78, 2846.26, 2676.43, 2598.44, 2597.68, 2729.39, 3027.34, 3137.54, 3171.69, 3234.1, 3331.61, 3409.88, 3636.28, 3783.2, 3895.01, 4078.33, 4220.49, 4273.51, 4251.55, 4124.53, 4094.92, 3946.01, 3693.86, 3360.16, 3000.72, 2983.37, 2875.63, 2791.94, 2845.71, 2952.3, 3119.45, 3262.34, 3212.01, 3373.31, 3497.5, 3606.31, 3782.65, 3978.03, 4157.28, 4329.95, 4487.17, 4470.97, 4313.16, 4166.59, 4203.84, 4099.21, 3755.24, 3455.81, 3309.54, 3148.3, 3003.57, 2883.05, 2857.45, 2967.44, 3261.29, 3349.52, 3402.9, 3521.49, 3659.96, 3854.52, 4076.5, 4269.69, 4376.1, 4490.33, 4615.58, 4681.86, 4578.43, 4356.58, 4309.4, 4158.83, 3996.97, 3790.93, 3588.42, 3456.68, 3355.82, 3217.15, 3197.59, 3259.59, 3357.61, 3433.81, 3544.34, 3735.7, 4013.68]. Please give me a forecast for the next 87 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_160.pkl | external_data/ground_truth_data/ground_truth_data_160.pkl | external_data/context/context_160.pkl | external_data/constraint/constraint_160.pkl |
161 | electricity_prediction_single-min_load | I have load_power data for the past 164 minutes. I require that the system load is maintained above a minimum of 12771.913978890225 MW. Please give me a forecast for the next 19 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 164 minutes. I require that the system load is maintained above a minimum of 12771.913978890225 MW. The historical load_power data for the past 164 minutes is: [10669.11, 11088.88, 12018.56, 13125.94, 14439.69, 15714.67, 16858.87, 17864.33, 18702.64, 19375.43, 19694.59, 19371.55, 18238.43, 16677.68, 15682.08, 14903.69, 13925.25, 12853.71, 11843.92, 11086.11, 10495.12, 10060.86, 9830.06, 9810.91, 9788.75, 10045.33, 10756.31, 11766.69, 12836.9, 13938.33, 15008.0, 15943.79, 16838.06, 17405.18, 17829.3, 17844.61, 17356.3, 16588.65, 16001.95, 15494.7, 14419.78, 13153.23, 12053.63, 11221.36, 10662.2, 10328.97, 10280.26, 10704.14, 11312.86, 11861.17, 12407.22, 12947.23, 13717.33, 14524.0, 15310.05, 16076.92, 16871.87, 17418.25, 17841.71, 18044.8, 17877.57, 17249.7, 16620.27, 16142.94, 14854.77, 13457.9, 12438.43, 11608.09, 11064.47, 10722.39, 10661.3, 11077.61, 11725.1, 12223.23, 12958.72, 14003.3, 15229.63, 16499.81, 17668.6, 18632.25, 18833.08, 18190.37, 17474.44, 17156.1, 17004.6, 16610.17, 16139.34, 15745.92, 14609.83, 13233.55, 12064.51, 11235.86, 10681.81, 10514.57, 10430.81, 10901.19, 11547.52, 12170.15, 13014.1, 14035.16, 15369.59, 16701.96, 17821.84, 18864.14, 19618.48, 20102.19, 20379.8, 20165.52, 19308.83, 18032.87, 16981.73, 16131.84, 14985.19, 13580.09, 12526.14, 11597.37, 11026.76, 10686.6, 10687.05, 11127.97, 11743.41, 12424.23, 13266.32, 14221.62, 15266.91, 16429.43, 17535.68, 18460.64, 19275.91, 20056.64, 20412.03, 20072.02, 18993.19, 17962.7, 17132.24, 16553.54, 15339.61, 13966.62, 12765.31, 11899.12, 11375.9, 11062.22, 10966.3, 11278.0, 11790.99, 12396.34, 13473.07, 14744.69, 16269.32, 17821.99, 19309.94, 20565.62, 21391.45, 21843.52, 22150.45, 22298.65, 22115.54, 21136.74, 20077.22, 19233.23, 18003.49, 16788.35, 15528.12, 14586.23]. Please give me a forecast for the next 19 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_161.pkl | external_data/ground_truth_data/ground_truth_data_161.pkl | external_data/context/context_161.pkl | external_data/constraint/constraint_161.pkl |
162 | electricity_prediction_single-min_load | I have load_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 1448.9065622061898 MW. Please give me a forecast for the next 45 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 1448.9065622061898 MW. The historical load_power data for the past 143 minutes is: [1817.35, 1795.02, 1806.43, 1838.27, 1896.93, 1888.66, 1843.58, 1788.08, 1701.29, 1619.71, 1559.15, 1528.79, 1512.13, 1520.46, 1524.48, 1591.49, 1720.7, 1820.55, 1817.79, 1754.59, 1720.11, 1684.78, 1663.31, 1656.37, 1644.51, 1673.66, 1707.27, 1742.68, 1777.7, 1769.56, 1734.62, 1691.98, 1623.19, 1535.57, 1480.25, 1450.18, 1436.44, 1417.88, 1417.02, 1432.65, 1480.49, 1546.04, 1592.48, 1616.25, 1643.69, 1641.1, 1602.32, 1565.99, 1543.61, 1565.0, 1589.36, 1619.64, 1661.4, 1653.25, 1632.12, 1598.25, 1548.48, 1489.87, 1447.26, 1418.02, 1392.5, 1376.76, 1371.87, 1375.26, 1406.32, 1445.43, 1483.89, 1489.9, 1484.25, 1481.56, 1472.08, 1449.81, 1437.54, 1456.0, 1514.1, 1601.65, 1696.93, 1711.72, 1695.08, 1663.53, 1610.93, 1564.26, 1511.67, 1474.06, 1453.81, 1448.31, 1452.0, 1479.38, 1549.74, 1684.66, 1768.93, 1807.4, 1807.15, 1806.04, 1799.62, 1789.59, 1698.96, 1730.23, 1756.53, 1807.86, 1849.46, 1836.92, 1805.52, 1761.0, 1693.35, 1614.42, 1541.53, 1496.32, 1477.64, 1465.42, 1459.94, 1474.28, 1543.66, 1654.19, 1729.22, 1757.64, 1770.08, 1773.32, 1759.29, 1755.57, 1779.32, 1787.88, 1787.27, 1813.13, 1861.1, 1848.25, 1826.36, 1785.06, 1723.53, 1657.45, 1592.6, 1538.36, 1512.74, 1506.02, 1505.84, 1518.39, 1577.49, 1702.5, 1774.68, 1788.83, 1802.06, 1812.77, 1801.74]. Please give me a forecast for the next 45 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_162.pkl | external_data/ground_truth_data/ground_truth_data_162.pkl | external_data/context/context_162.pkl | external_data/constraint/constraint_162.pkl |
163 | electricity_prediction_single-min_load | I have load_power data for the past 197 minutes. I require that the system load is maintained above a minimum of 197.73528783584916 MW. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 197 minutes. I require that the system load is maintained above a minimum of 197.73528783584916 MW. The historical load_power data for the past 197 minutes is: [292.89, 327.57, 325.35, 304.99, 285.54, 283.83, 280.46, 278.8, 271.35, 272.14, 272.82, 289.84, 319.47, 346.67, 350.88, 341.05, 325.28, 302.59, 277.48, 276.62, 269.07, 267.03, 265.55, 274.35, 292.17, 320.16, 329.15, 326.32, 332.54, 330.25, 313.84, 305.24, 301.3, 307.34, 314.9, 325.76, 341.61, 346.17, 341.01, 327.0, 312.65, 298.85, 281.1, 265.41, 255.76, 250.79, 249.11, 250.4, 254.22, 271.14, 289.95, 304.9, 313.15, 315.22, 315.27, 307.7, 299.45, 291.34, 303.86, 317.03, 330.89, 339.1, 333.1, 324.62, 317.09, 296.98, 274.41, 273.31, 266.17, 261.64, 260.51, 265.92, 274.2, 281.9, 285.06, 277.16, 279.38, 278.05, 271.52, 272.31, 268.38, 271.92, 277.93, 289.58, 306.69, 324.97, 315.67, 300.03, 286.04, 263.03, 248.6, 239.04, 237.09, 239.52, 243.83, 260.15, 295.16, 320.17, 334.52, 336.34, 336.9, 333.52, 328.24, 329.8, 324.5, 325.63, 335.94, 350.93, 358.22, 363.49, 349.21, 335.53, 308.54, 278.36, 265.64, 263.03, 258.7, 257.41, 261.12, 271.26, 295.59, 320.82, 329.08, 340.99, 348.33, 353.73, 359.68, 365.23, 362.41, 363.76, 369.94, 378.93, 396.03, 400.73, 385.98, 366.35, 334.26, 301.44, 292.84, 287.96, 279.77, 275.37, 285.92, 304.78, 333.27, 354.19, 361.09, 356.0, 348.24, 333.7, 316.8, 309.42, 301.4, 295.65, 309.43, 330.37, 355.32, 375.49, 369.36, 352.28, 323.34, 293.78, 283.96, 277.1, 276.57, 276.35, 284.75, 305.5, 337.74, 354.02, 348.82, 343.63, 344.07, 317.66, 293.79, 283.54, 274.95, 273.35, 274.45, 292.09, 314.08, 326.88, 327.43, 321.81, 299.94, 270.51, 259.54, 254.12, 250.26, 251.14, 255.21, 268.03, 299.78, 326.22, 329.06, 328.98, 334.37]. Please give me a forecast for the next 34 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_163.pkl | external_data/ground_truth_data/ground_truth_data_163.pkl | external_data/context/context_163.pkl | external_data/constraint/constraint_163.pkl |
164 | electricity_prediction_single-min_load | I have load_power data for the past 140 minutes. I require that the system load is maintained above a minimum of 14276.430772798076 MW. Please give me a forecast for the next 11 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 140 minutes. I require that the system load is maintained above a minimum of 14276.430772798076 MW. The historical load_power data for the past 140 minutes is: [15841.48, 16099.53, 16910.03, 18022.64, 18888.98, 18951.51, 18736.27, 18651.68, 18449.48, 18327.44, 18403.76, 18353.82, 18397.05, 18857.68, 19184.73, 18931.62, 18571.8, 18211.54, 17711.84, 17060.31, 16346.66, 15768.96, 15368.14, 15167.98, 15133.03, 15432.01, 16023.84, 16924.53, 17865.04, 18128.71, 18221.71, 18296.13, 18289.88, 18071.4, 17944.08, 17770.65, 17667.56, 17977.91, 18094.34, 17641.49, 17294.06, 17006.33, 16533.02, 16006.06, 15358.89, 14855.91, 14465.34, 14264.2, 14176.65, 14235.95, 14534.87, 14999.78, 15628.11, 16196.6, 16594.31, 16709.45, 16602.85, 16402.36, 16272.72, 16259.88, 16402.74, 16908.18, 17351.97, 17216.21, 17025.35, 16749.4, 16332.41, 15751.89, 15147.96, 14676.05, 14313.35, 14139.12, 14075.17, 14052.01, 14226.57, 14586.78, 15189.15, 15752.2, 16121.54, 16287.18, 16420.72, 16431.36, 16300.85, 16189.04, 16131.7, 16330.02, 16480.45, 16243.43, 15908.57, 15645.7, 15411.95, 15106.8, 14631.75, 14140.09, 13795.21, 13591.12, 13582.25, 13728.66, 13985.21, 14252.27, 14828.5, 15400.57, 15808.79, 16030.85, 16121.99, 15980.25, 15795.62, 15722.1, 15776.57, 16134.71, 16591.09, 16444.72, 16227.28, 15986.94, 15621.78, 15198.39, 14705.42, 14307.34, 13980.25, 13856.39, 13895.68, 14139.16, 14706.45, 15440.69, 16145.56, 16692.25, 17110.86, 17406.17, 17677.95, 17737.98, 17796.12, 17778.94, 17791.89, 18098.43, 18476.32, 18221.86, 17856.88, 17470.48, 16906.66, 16347.26]. Please give me a forecast for the next 11 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_164.pkl | external_data/ground_truth_data/ground_truth_data_164.pkl | external_data/context/context_164.pkl | external_data/constraint/constraint_164.pkl |
165 | electricity_prediction_single-min_load | I have load_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 3813.653185874226 MW. Please give me a forecast for the next 73 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 143 minutes. I require that the system load is maintained above a minimum of 3813.653185874226 MW. The historical load_power data for the past 143 minutes is: [4727.25, 4520.14, 4304.58, 4142.12, 4011.62, 3949.24, 3941.49, 3995.58, 4106.74, 4244.97, 4409.95, 4539.56, 4641.69, 4697.73, 4712.16, 4706.59, 4699.47, 4702.55, 4757.26, 4849.31, 4960.8, 5088.27, 5122.97, 5020.98, 4800.75, 4560.72, 4345.97, 4194.91, 4101.41, 4061.68, 4122.44, 4356.44, 4794.96, 5204.01, 5412.08, 5498.13, 5487.91, 5475.16, 5461.01, 5429.06, 5396.58, 5416.97, 5511.28, 5567.57, 5482.14, 5485.38, 5393.93, 5202.24, 4903.26, 4579.86, 4299.14, 4115.31, 4010.06, 3953.62, 3993.7, 4207.34, 4639.42, 5038.47, 5283.09, 5394.68, 5389.35, 5407.65, 5429.4, 5454.72, 5498.45, 5560.11, 5599.9, 5622.51, 5504.81, 5471.29, 5361.53, 5180.13, 4893.47, 4572.12, 4276.58, 4087.98, 3976.99, 3914.08, 3943.61, 4139.9, 4580.41, 5018.36, 5340.24, 5531.28, 5600.16, 5640.59, 5652.86, 5653.84, 5637.63, 5641.74, 5671.49, 5673.34, 5563.04, 5502.9, 5365.27, 5180.96, 4907.65, 4600.67, 4343.69, 4156.15, 4039.75, 3985.0, 4009.34, 4184.21, 4562.88, 4929.98, 5215.13, 5342.42, 5370.02, 5422.73, 5426.19, 5547.87, 5570.69, 5583.42, 5664.76, 5645.1, 5549.41, 5469.94, 5291.8, 5090.42, 4811.53, 4519.19, 4270.52, 4090.21, 3952.84, 3882.38, 3890.67, 4039.69, 4376.85, 4689.3, 4926.29, 5125.24, 5217.96, 5223.09, 5202.96, 5274.77, 5242.68, 5237.39, 5301.08, 5305.52, 5267.75, 5243.9, 5147.15]. Please give me a forecast for the next 73 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_165.pkl | external_data/ground_truth_data/ground_truth_data_165.pkl | external_data/context/context_165.pkl | external_data/constraint/constraint_165.pkl |
166 | electricity_prediction_single-min_load | I have load_power data for the past 86 minutes. I require that the system load is maintained above a minimum of 1471.8017036239214 MW. Please give me a forecast for the next 10 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 86 minutes. I require that the system load is maintained above a minimum of 1471.8017036239214 MW. The historical load_power data for the past 86 minutes is: [2135.59, 2263.46, 2399.53, 2484.43, 2559.53, 2578.54, 2557.7, 2572.12, 2559.11, 2565.6, 2561.93, 2615.03, 2664.37, 2670.36, 2554.61, 2373.64, 2173.92, 1982.3, 1847.22, 1752.3, 1695.53, 1660.04, 1670.28, 1761.48, 1925.15, 2008.56, 2024.12, 2001.42, 1982.04, 1986.47, 2043.51, 2147.8, 2271.93, 2415.96, 2561.92, 2658.97, 2660.36, 2652.52, 2541.54, 2363.77, 2155.19, 1955.22, 1810.65, 1713.5, 1653.84, 1616.21, 1631.84, 1725.51, 1881.31, 1956.22, 1977.84, 1946.65, 1913.65, 1926.54, 1936.3, 2030.93, 2162.89, 2322.7, 2511.87, 2640.04, 2654.0, 2635.38, 2529.87, 2344.22, 2139.63, 1941.15, 1802.56, 1709.63, 1652.95, 1622.17, 1635.66, 1724.82, 1871.34, 1949.75, 1956.87, 1915.01, 1881.92, 1888.43, 1929.08, 2023.73, 2140.56, 2298.44, 2453.52, 2563.98, 2568.7, 2590.06]. Please give me a forecast for the next 10 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_166.pkl | external_data/ground_truth_data/ground_truth_data_166.pkl | external_data/context/context_166.pkl | external_data/constraint/constraint_166.pkl |
167 | electricity_prediction_single-min_load | I have load_power data for the past 103 minutes. I require that the system load is maintained above a minimum of 1038.5087846376796 MW. Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 103 minutes. I require that the system load is maintained above a minimum of 1038.5087846376796 MW. The historical load_power data for the past 103 minutes is: [1038.52, 1004.86, 980.12, 974.35, 986.43, 996.8, 1018.2, 1060.28, 1091.47, 1124.21, 1155.08, 1184.22, 1228.15, 1265.64, 1309.68, 1345.86, 1370.21, 1369.7, 1319.09, 1276.94, 1227.19, 1162.56, 1096.43, 1018.75, 979.57, 953.2, 938.76, 935.16, 946.31, 972.02, 1002.18, 1037.31, 1070.27, 1057.84, 1048.07, 1052.65, 1069.43, 1103.85, 1149.1, 1190.06, 1230.91, 1240.08, 1206.53, 1190.89, 1159.19, 1091.65, 1025.66, 987.16, 959.59, 946.43, 939.55, 951.17, 986.53, 1055.92, 1088.63, 1078.26, 1086.11, 1093.78, 1114.8, 1118.48, 1137.61, 1171.6, 1190.78, 1222.57, 1228.9, 1209.94, 1204.64, 1217.04, 1183.04, 1121.85, 1059.04, 1000.4, 968.66, 953.81, 946.88, 950.0, 984.0, 1053.71, 1085.35, 1085.66, 1089.93, 1113.06, 1135.06, 1158.06, 1204.09, 1260.58, 1314.18, 1360.92, 1391.3, 1402.68, 1385.18, 1376.37, 1331.39, 1260.66, 1182.78, 1148.54, 1109.74, 1085.59, 1062.58, 1059.95, 1082.08, 1144.83, 1161.25]. Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_167.pkl | external_data/ground_truth_data/ground_truth_data_167.pkl | external_data/context/context_167.pkl | external_data/constraint/constraint_167.pkl |
168 | electricity_prediction_single-load_ramp_rate | I have wind_power data for the past 76 minutes. I must monitor the load ramp rate to ensure it does not exceed 1377.3323386480533 MW for each time step. Please give me a forecast for the next 20 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have wind_power data for the past 76 minutes. I must monitor the load ramp rate to ensure it does not exceed 1377.3323386480533 MW for each time step. The historical wind_power data for the past 76 minutes is: [2584.5, 2289.6, 2206.6, 2576.0, 3026.7, 2635.0, 1867.6, 1239.3, 1552.6, 1707.7, 1895.9, 2312.8, 2679.4, 2520.9, 2846.0, 3623.5, 5247.0, 6835.7, 7854.0, 9012.4, 9594.7, 9936.2, 9848.9, 9473.1, 10036.5, 10656.1, 11349.2, 11872.1, 12484.2, 12544.6, 12302.2, 12895.5, 12922.7, 12504.2, 12398.8, 12384.9, 12938.4, 13679.5, 14707.2, 14541.4, 14403.1, 13705.5, 12491.1, 12511.7, 11562.9, 11088.5, 11616.8, 11674.7, 12155.3, 12555.4, 13039.1, 13704.4, 13081.7, 12545.3, 11863.5, 11206.8, 10605.5, 11207.4, 11797.0, 11608.4, 10231.6, 7634.6, 6058.7, 5582.8, 5028.3, 4583.3, 4796.7, 4835.0, 4623.9, 4674.6, 4881.0, 5110.3, 5407.4, 5839.9, 6290.2, 6781.5]. Please give me a forecast for the next 20 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL. | external_data/executor_variables/executor_variables_168.pkl | external_data/ground_truth_data/ground_truth_data_168.pkl | external_data/context/context_168.pkl | external_data/constraint/constraint_168.pkl |
169 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 85 minutes. I must monitor the load ramp rate to ensure it does not exceed 125.83353093189945 MW for each time step. Please give me a forecast for the next 31 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 85 minutes. I must monitor the load ramp rate to ensure it does not exceed 125.83353093189945 MW for each time step. The historical load_power data for the past 85 minutes is: [1112.74, 1131.06, 1157.06, 1197.65, 1233.86, 1232.14, 1201.04, 1178.39, 1157.31, 1167.42, 1167.05, 1164.74, 1189.33, 1220.91, 1275.68, 1282.61, 1256.28, 1229.8, 1176.69, 1123.21, 1066.09, 1011.66, 974.4, 966.85, 962.78, 961.0, 971.43, 1003.44, 1031.23, 1045.16, 1038.5, 1008.4, 950.22, 891.96, 879.99, 912.76, 945.66, 989.12, 1037.42, 1046.18, 1029.11, 987.73, 935.11, 882.29, 836.35, 794.36, 777.44, 777.31, 778.7, 801.09, 864.0, 961.16, 1020.28, 1021.84, 1014.14, 1005.42, 959.71, 928.65, 910.54, 896.8, 921.59, 1010.54, 1095.76, 1115.37, 1101.5, 1073.05, 1025.94, 967.11, 917.59, 883.71, 870.36, 872.13, 881.72, 914.15, 979.32, 1087.96, 1122.35, 1054.26, 978.22, 931.26, 905.89, 895.47, 937.34, 1004.85, 1035.43]. Please give me a forecast for the next 31 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_169.pkl | external_data/ground_truth_data/ground_truth_data_169.pkl | external_data/context/context_169.pkl | external_data/constraint/constraint_169.pkl |
170 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 182 minutes. I must monitor the load ramp rate to ensure it does not exceed 121.1349276759708 MW for each time step. Please give me a forecast for the next 32 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 182 minutes. I must monitor the load ramp rate to ensure it does not exceed 121.1349276759708 MW for each time step. The historical load_power data for the past 182 minutes is: [1724.88, 1645.35, 1604.56, 1575.16, 1568.78, 1566.41, 1594.29, 1658.9, 1758.15, 1840.95, 1842.15, 1830.44, 1825.95, 1815.03, 1821.23, 1874.41, 1854.64, 1831.46, 1840.7, 1895.14, 1891.85, 1871.08, 1833.98, 1769.42, 1684.71, 1618.09, 1558.02, 1529.59, 1516.78, 1507.97, 1521.24, 1582.22, 1692.92, 1780.31, 1799.55, 1816.95, 1846.58, 1827.72, 1817.47, 1831.63, 1820.33, 1818.17, 1828.66, 1871.57, 1871.82, 1854.32, 1820.46, 1752.41, 1682.14, 1609.14, 1556.87, 1530.42, 1528.16, 1541.09, 1568.44, 1640.84, 1761.07, 1848.75, 1885.82, 1902.24, 1915.73, 1926.6, 1934.1, 1946.44, 1932.42, 1928.76, 1939.91, 1981.89, 1962.66, 1926.24, 1889.36, 1840.49, 1772.63, 1703.16, 1643.42, 1608.14, 1588.56, 1581.65, 1588.65, 1611.78, 1666.11, 1729.07, 1776.83, 1832.52, 1855.5, 1853.91, 1850.97, 1836.52, 1822.95, 1813.41, 1835.05, 1908.69, 1922.0, 1896.26, 1870.7, 1822.3, 1757.13, 1697.47, 1629.36, 1600.38, 1586.8, 1574.36, 1574.23, 1584.63, 1615.5, 1654.61, 1693.59, 1722.3, 1746.92, 1737.81, 1715.36, 1674.58, 1622.39, 1650.39, 1678.7, 1802.9, 1835.0, 1819.99, 1805.7, 1765.04, 1707.88, 1652.42, 1610.27, 1595.77, 1584.72, 1590.89, 1618.01, 1668.45, 1761.68, 1845.21, 1877.74, 1846.11, 1819.04, 1798.98, 1759.75, 1742.15, 1732.9, 1746.8, 1812.34, 1903.7, 1915.07, 1887.63, 1840.93, 1780.31, 1708.66, 1640.47, 1582.56, 1544.27, 1525.22, 1517.01, 1534.63, 1614.44, 1727.38, 1810.43, 1839.36, 1831.55, 1860.35, 1867.7, 1860.96, 1857.28, 1829.49, 1832.95, 1864.16, 1893.59, 1880.0, 1843.98, 1797.88, 1743.32, 1662.41, 1598.13, 1546.64, 1533.77, 1521.31, 1514.31, 1536.91, 1596.98, 1711.71, 1810.49, 1827.72, 1849.68, 1871.67, 1870.63]. Please give me a forecast for the next 32 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_170.pkl | external_data/ground_truth_data/ground_truth_data_170.pkl | external_data/context/context_170.pkl | external_data/constraint/constraint_170.pkl |
171 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 158 minutes. I must monitor the load ramp rate to ensure it does not exceed 124.29515262390282 MW for each time step. Please give me a forecast for the next 31 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 158 minutes. I must monitor the load ramp rate to ensure it does not exceed 124.29515262390282 MW for each time step. The historical load_power data for the past 158 minutes is: [1024.69, 1029.95, 1030.9, 1022.99, 1018.32, 1010.01, 1003.22, 1026.94, 1059.52, 1044.67, 1022.12, 980.19, 914.96, 857.2, 786.47, 757.87, 724.04, 710.05, 709.45, 721.01, 774.07, 881.11, 940.33, 970.93, 976.35, 974.23, 983.01, 978.6, 988.6, 984.52, 998.53, 1037.58, 1065.91, 1057.03, 1048.76, 1024.46, 991.06, 941.46, 896.06, 863.14, 843.03, 835.0, 835.56, 843.74, 867.4, 919.12, 975.01, 998.3, 1023.61, 1014.62, 1015.69, 1002.6, 996.77, 1005.5, 1019.72, 1072.04, 1122.0, 1129.18, 1103.71, 1084.09, 1045.18, 1006.28, 973.38, 930.49, 907.1, 892.55, 897.25, 901.78, 922.21, 953.25, 990.98, 1020.0, 1020.49, 1006.92, 987.16, 951.22, 917.61, 911.03, 960.51, 1039.86, 1122.63, 1123.36, 1102.24, 1087.61, 1048.41, 1000.37, 955.62, 929.65, 917.08, 914.61, 921.05, 940.71, 983.75, 1060.15, 1111.4, 1115.39, 1064.87, 1013.29, 973.29, 943.68, 924.75, 922.46, 966.88, 1062.85, 1137.57, 1147.45, 1135.31, 1115.38, 1065.35, 1004.74, 946.44, 910.99, 893.53, 891.37, 897.74, 913.83, 971.97, 1078.87, 1128.28, 1103.99, 1084.04, 1095.69, 1108.19, 1100.03, 1093.39, 1069.03, 1064.62, 1095.55, 1131.16, 1122.35, 1103.17, 1045.89, 977.69, 912.32, 857.43, 822.97, 797.02, 793.33, 793.22, 807.68, 859.07, 950.5, 996.66, 986.66, 982.77, 983.98, 979.63, 977.99, 976.89, 970.38, 972.89, 1019.48, 1065.19, 1047.43, 1024.32, 988.59, 932.96, 878.33]. Please give me a forecast for the next 31 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_171.pkl | external_data/ground_truth_data/ground_truth_data_171.pkl | external_data/context/context_171.pkl | external_data/constraint/constraint_171.pkl |
172 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 102 minutes. I must monitor the load ramp rate to ensure it does not exceed 191.67208280220598 MW for each time step. Please give me a forecast for the next 29 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 102 minutes. I must monitor the load ramp rate to ensure it does not exceed 191.67208280220598 MW for each time step. The historical load_power data for the past 102 minutes is: [2271.67, 2228.78, 2103.81, 1937.93, 1782.12, 1669.73, 1593.51, 1556.64, 1537.65, 1571.91, 1691.54, 1873.97, 2020.5, 2106.69, 2164.37, 2169.73, 2108.97, 1926.56, 1779.21, 1744.16, 1882.2, 1932.37, 2064.11, 2158.44, 2268.72, 2266.21, 2146.13, 1989.0, 1840.45, 1736.19, 1668.66, 1640.08, 1629.69, 1678.49, 1802.1, 1988.53, 2067.51, 1963.44, 1808.57, 1683.25, 1591.21, 1527.38, 1498.35, 1508.13, 1585.29, 1706.27, 1915.86, 2071.08, 2235.77, 2254.86, 2146.13, 1986.05, 1826.26, 1703.73, 1642.41, 1614.57, 1619.88, 1690.2, 1830.23, 2030.44, 2115.59, 2025.45, 1887.11, 1766.43, 1686.7, 1625.51, 1591.27, 1590.03, 1636.17, 1760.63, 1948.93, 2150.31, 2308.13, 2327.67, 2232.97, 2061.4, 1914.13, 1815.57, 1754.54, 1730.49, 1726.42, 1770.39, 1896.69, 2054.05, 2118.66, 2032.95, 2022.29, 1947.47, 1839.11, 1845.76, 2036.09, 2096.19, 2054.59, 2002.48, 2093.71, 2182.53, 2250.29, 2204.89, 2093.02, 1953.1, 1809.16, 1689.7]. Please give me a forecast for the next 29 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_172.pkl | external_data/ground_truth_data/ground_truth_data_172.pkl | external_data/context/context_172.pkl | external_data/constraint/constraint_172.pkl |
173 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 87 minutes. I must monitor the load ramp rate to ensure it does not exceed 89.08490769564426 MW for each time step. Please give me a forecast for the next 81 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 87 minutes. I must monitor the load ramp rate to ensure it does not exceed 89.08490769564426 MW for each time step. The historical load_power data for the past 87 minutes is: [1012.51, 989.57, 962.41, 960.44, 966.85, 982.22, 1026.52, 1120.77, 1147.11, 1144.3, 1149.6, 1137.09, 1134.66, 1138.41, 1163.28, 1188.83, 1176.56, 1168.27, 1154.07, 1145.72, 1144.01, 1144.24, 1116.14, 1069.68, 1025.55, 1025.04, 1021.31, 1025.35, 1036.73, 1065.95, 1134.69, 1244.56, 1277.78, 1232.75, 1188.87, 1134.41, 1109.75, 1085.29, 1069.15, 1069.57, 1071.01, 1085.61, 1092.04, 1103.05, 1121.58, 1124.89, 1104.07, 1063.08, 1039.74, 1010.23, 1012.63, 1020.59, 1028.12, 1044.19, 1072.11, 1117.97, 1164.52, 1160.09, 1123.0, 1089.94, 1060.78, 1046.94, 1049.85, 1071.35, 1085.28, 1098.85, 1111.4, 1109.59, 1096.0, 1074.56, 1054.0, 1017.46, 985.33, 961.99, 949.24, 944.78, 944.08, 953.27, 976.87, 1013.64, 1038.95, 1057.21, 1057.01, 1049.39, 1045.35, 1059.14, 1077.27]. Please give me a forecast for the next 81 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_173.pkl | external_data/ground_truth_data/ground_truth_data_173.pkl | external_data/context/context_173.pkl | external_data/constraint/constraint_173.pkl |
174 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 118 minutes. I must monitor the load ramp rate to ensure it does not exceed 50.78587107473811 MW for each time step. Please give me a forecast for the next 25 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 118 minutes. I must monitor the load ramp rate to ensure it does not exceed 50.78587107473811 MW for each time step. The historical load_power data for the past 118 minutes is: [694.17, 646.68, 620.22, 645.19, 748.18, 841.37, 898.27, 921.55, 885.39, 829.05, 770.66, 746.66, 740.01, 736.01, 739.5, 762.21, 818.99, 919.09, 952.21, 884.41, 782.95, 728.97, 713.23, 681.14, 650.02, 604.94, 591.16, 636.4, 708.72, 798.32, 870.78, 869.63, 828.37, 778.53, 724.57, 701.11, 692.91, 689.88, 694.74, 717.73, 779.1, 877.24, 907.79, 856.02, 763.27, 673.37, 636.29, 614.73, 596.14, 583.86, 632.65, 714.77, 777.96, 824.09, 862.68, 854.79, 816.54, 765.7, 697.45, 680.78, 660.25, 648.11, 645.23, 661.22, 709.46, 805.58, 859.04, 870.96, 862.09, 864.85, 865.64, 853.08, 840.34, 808.2, 818.21, 833.77, 853.15, 874.68, 899.57, 885.7, 844.37, 786.97, 722.56, 687.68, 668.84, 663.28, 665.33, 686.82, 740.12, 840.86, 891.5, 876.07, 823.78, 747.25, 697.34, 685.5, 652.5, 611.15, 590.56, 614.23, 678.57, 765.84, 843.33, 850.61, 832.09, 794.44, 737.52, 711.76, 703.06, 691.39, 688.59, 704.98, 729.89, 772.12, 806.27, 845.76, 883.35, 903.02]. Please give me a forecast for the next 25 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_174.pkl | external_data/ground_truth_data/ground_truth_data_174.pkl | external_data/context/context_174.pkl | external_data/constraint/constraint_174.pkl |
175 | electricity_prediction_single-load_ramp_rate | I have wind_power data for the past 99 minutes. I must monitor the load ramp rate to ensure it does not exceed 2807.811166518407 MW for each time step. Please give me a forecast for the next 62 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have wind_power data for the past 99 minutes. I must monitor the load ramp rate to ensure it does not exceed 2807.811166518407 MW for each time step. The historical wind_power data for the past 99 minutes is: [10093.2, 9804.0, 9682.1, 9761.8, 9940.2, 10013.6, 10118.4, 10101.5, 9948.3, 10090.8, 10687.6, 10890.7, 11408.2, 11821.3, 12311.9, 12246.3, 12052.3, 11872.9, 11414.2, 10278.5, 9505.4, 8693.9, 8150.4, 7438.1, 6659.7, 6003.3, 5618.9, 5490.2, 5174.3, 4124.3, 3042.2, 2489.1, 2482.5, 2471.7, 2398.6, 1928.5, 1595.8, 1316.8, 1237.9, 1257.3, 1232.2, 1225.4, 1256.0, 1503.6, 2188.8, 3226.8, 4126.6, 5161.2, 6207.6, 7273.0, 8365.3, 8967.1, 8226.0, 5103.1, 3786.8, 3608.6, 3589.3, 3910.6, 4108.7, 4020.4, 4056.2, 4038.8, 3834.6, 3056.5, 2444.5, 2341.8, 2803.8, 3789.1, 4489.9, 4674.2, 4811.9, 4984.6, 5260.7, 6074.1, 6202.1, 5721.0, 5179.0, 4495.9, 3322.8, 3004.2, 3254.6, 3816.9, 3872.3, 4144.9, 4604.6, 4581.0, 5513.5, 6726.0, 7946.8, 9048.3, 10523.5, 11481.1, 11978.5, 12261.1, 12210.3, 11861.8, 11382.2, 11250.6, 11024.3]. Please give me a forecast for the next 62 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL. | external_data/executor_variables/executor_variables_175.pkl | external_data/ground_truth_data/ground_truth_data_175.pkl | external_data/context/context_175.pkl | external_data/constraint/constraint_175.pkl |
176 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 77 minutes. I must monitor the load ramp rate to ensure it does not exceed 695.394644787965 MW for each time step. Please give me a forecast for the next 39 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 77 minutes. I must monitor the load ramp rate to ensure it does not exceed 695.394644787965 MW for each time step. The historical load_power data for the past 77 minutes is: [7068.54, 6860.69, 6658.39, 6359.83, 6049.72, 5731.33, 5559.22, 5455.39, 5420.09, 5454.07, 5624.02, 5955.72, 6177.56, 6314.97, 6267.31, 6191.1, 6190.62, 6229.19, 6389.74, 6602.34, 6895.25, 7170.61, 7338.49, 7250.72, 7165.21, 6910.75, 6632.22, 6323.55, 5951.22, 5642.99, 5423.13, 5252.59, 5181.65, 5163.42, 5245.9, 5421.03, 5603.54, 5884.09, 6068.74, 6125.0, 6217.23, 6361.43, 6602.09, 6917.63, 7253.29, 7542.09, 7731.81, 7704.53, 7725.38, 7537.39, 7151.35, 6612.07, 6081.98, 5637.71, 5410.58, 5326.42, 5289.58, 5375.69, 5695.15, 6289.22, 6594.18, 6700.19, 6855.05, 6981.92, 7172.13, 7393.65, 7626.95, 7850.03, 8079.2, 8398.05, 8595.91, 8569.86, 8446.49, 8130.46, 7708.66, 7160.96, 6588.84]. Please give me a forecast for the next 39 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_176.pkl | external_data/ground_truth_data/ground_truth_data_176.pkl | external_data/context/context_176.pkl | external_data/constraint/constraint_176.pkl |
177 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 61 minutes. I must monitor the load ramp rate to ensure it does not exceed 1795.4525592785355 MW for each time step. Please give me a forecast for the next 36 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 61 minutes. I must monitor the load ramp rate to ensure it does not exceed 1795.4525592785355 MW for each time step. The historical load_power data for the past 61 minutes is: [25011.92, 23972.81, 22953.8, 21939.17, 20593.41, 19254.41, 18247.44, 17439.39, 16722.55, 16183.42, 15957.46, 15783.31, 15759.22, 16512.57, 17978.86, 19710.49, 21346.89, 22809.8, 23840.96, 24371.14, 24458.61, 23503.99, 22483.51, 21665.31, 20841.72, 20394.3, 19702.06, 18416.67, 16966.44, 15761.25, 15009.57, 14566.1, 14214.01, 14192.44, 14585.71, 15227.11, 15459.56, 16015.51, 17098.72, 18445.39, 19765.52, 20857.01, 21786.13, 22543.46, 23208.04, 23667.89, 23478.49, 22717.42, 21296.55, 20295.53, 19218.69, 17661.37, 16084.93, 14904.4, 13993.36, 13366.37, 13045.04, 12961.44, 13320.15, 14120.42, 14306.66]. Please give me a forecast for the next 36 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_177.pkl | external_data/ground_truth_data/ground_truth_data_177.pkl | external_data/context/context_177.pkl | external_data/constraint/constraint_177.pkl |
178 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 124 minutes. I must monitor the load ramp rate to ensure it does not exceed 1187.6020380539794 MW for each time step. Please give me a forecast for the next 72 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 124 minutes. I must monitor the load ramp rate to ensure it does not exceed 1187.6020380539794 MW for each time step. The historical load_power data for the past 124 minutes is: [11439.95, 11839.11, 12759.71, 14131.22, 14782.27, 14619.12, 14367.76, 13780.83, 13181.4, 12837.49, 12485.94, 12213.18, 12059.94, 12273.24, 13093.06, 13471.35, 13382.89, 13250.4, 12819.6, 11973.77, 11211.11, 10454.32, 10122.81, 9921.61, 9913.0, 10191.13, 10847.33, 12002.83, 12656.16, 12841.85, 13159.13, 13362.76, 13409.57, 13427.14, 13315.72, 13160.97, 13138.81, 13228.4, 13789.69, 13875.6, 13646.37, 13367.04, 13133.34, 12705.01, 12182.84, 11818.48, 11609.51, 11555.48, 11714.45, 11913.68, 12431.48, 13119.97, 13827.76, 14106.91, 13835.44, 13295.86, 12711.19, 12184.71, 11697.28, 11434.97, 11373.96, 11664.41, 12485.79, 13008.04, 13094.42, 13199.21, 13246.79, 13093.19, 12735.7, 12541.56, 12375.28, 12461.48, 12535.69, 12744.74, 13208.12, 13763.23, 14376.84, 14373.85, 13647.6, 12798.17, 12121.61, 11699.31, 11347.08, 11145.74, 11107.3, 11313.43, 12063.74, 12621.34, 12681.05, 12625.57, 12349.61, 11822.48, 11275.01, 10813.97, 10600.72, 10580.21, 10788.19, 11224.21, 12252.57, 13738.18, 14450.0, 14160.13, 13786.05, 13339.76, 12883.88, 12505.47, 12264.21, 12042.74, 12013.2, 12250.36, 13110.7, 13778.0, 13799.61, 13812.3, 13604.98, 13134.59, 12677.11, 12354.61, 12284.03, 12341.17, 12560.22, 13203.81, 14296.25, 15762.18]. Please give me a forecast for the next 72 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_178.pkl | external_data/ground_truth_data/ground_truth_data_178.pkl | external_data/context/context_178.pkl | external_data/constraint/constraint_178.pkl |
179 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 186 minutes. I must monitor the load ramp rate to ensure it does not exceed 157.2472005601813 MW for each time step. Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 186 minutes. I must monitor the load ramp rate to ensure it does not exceed 157.2472005601813 MW for each time step. The historical load_power data for the past 186 minutes is: [2138.41, 2011.3, 1892.61, 1810.96, 1756.98, 1724.36, 1720.79, 1751.0, 1809.01, 1904.49, 1969.28, 2004.8, 2034.32, 2074.37, 2114.0, 2150.35, 2196.65, 2218.48, 2226.52, 2221.29, 2202.66, 2164.18, 2072.44, 2004.13, 1891.77, 1748.1, 1635.79, 1563.67, 1503.4, 1465.29, 1447.43, 1439.04, 1439.58, 1482.39, 1539.52, 1586.57, 1628.53, 1676.81, 1725.84, 1785.4, 1850.19, 1917.97, 1978.04, 1960.59, 1917.57, 1879.14, 1855.06, 1832.85, 1763.8, 1674.91, 1591.63, 1530.83, 1491.36, 1461.75, 1446.0, 1438.73, 1425.79, 1465.21, 1530.5, 1601.72, 1657.58, 1709.93, 1757.64, 1804.79, 1846.53, 1871.29, 1901.83, 1940.37, 1946.7, 1939.26, 1892.5, 1869.88, 1792.98, 1692.08, 1617.92, 1559.55, 1525.89, 1502.31, 1512.01, 1556.98, 1627.0, 1735.98, 1819.29, 1862.64, 1879.76, 1911.94, 1954.81, 2010.91, 2028.83, 2050.7, 2111.14, 2163.52, 2157.91, 2113.21, 2041.12, 1989.63, 1896.15, 1778.63, 1688.66, 1605.11, 1555.44, 1528.97, 1531.88, 1555.85, 1607.83, 1724.93, 1801.41, 1894.66, 1982.1, 2051.34, 2098.09, 2128.72, 2153.73, 2192.08, 2235.59, 2251.18, 2183.25, 2129.67, 2077.95, 2007.63, 1872.46, 1749.91, 1656.78, 1589.33, 1548.76, 1522.15, 1506.0, 1532.38, 1584.93, 1682.08, 1761.05, 1807.95, 1839.44, 1880.78, 1952.87, 2031.87, 2099.51, 2159.71, 2196.85, 2215.56, 2191.2, 2114.25, 2043.58, 2003.05, 1898.11, 1785.09, 1675.95, 1621.13, 1582.32, 1558.46, 1562.78, 1614.55, 1681.19, 1800.82, 1886.76, 1964.21, 2010.18, 2024.41, 2023.68, 2076.07, 2091.88, 2112.33, 2110.09, 2106.23, 2085.87, 2060.21, 2005.8, 1975.2, 1877.34, 1752.98, 1665.42, 1581.51, 1531.7, 1508.61, 1511.14, 1545.6, 1600.58, 1683.23, 1788.99, 1853.15, 1920.23, 1980.38, 2038.0, 2115.02, 2146.32, 2199.64]. Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_179.pkl | external_data/ground_truth_data/ground_truth_data_179.pkl | external_data/context/context_179.pkl | external_data/constraint/constraint_179.pkl |
180 | electricity_prediction_single-load_ramp_rate | I have wind_power data for the past 128 minutes. I must monitor the load ramp rate to ensure it does not exceed 1486.9559655876728 MW for each time step. Please give me a forecast for the next 51 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have wind_power data for the past 128 minutes. I must monitor the load ramp rate to ensure it does not exceed 1486.9559655876728 MW for each time step. The historical wind_power data for the past 128 minutes is: [2089.3, 1807.6, 1825.5, 1932.7, 1447.4, 1147.7, 1162.3, 1194.4, 1258.2, 1131.2, 820.0, 746.2, 803.2, 997.8, 1698.0, 3098.9, 3039.3, 2911.1, 2719.7, 1792.4, 1011.7, 421.7, 176.0, 131.5, 209.1, 208.7, 438.0, 447.5, 321.4, 353.3, 608.3, 987.2, 1295.4, 1557.7, 1443.7, 1461.2, 2027.2, 1094.0, 1098.6, 1247.6, 1724.4, 1501.9, 577.3, 385.2, 629.7, 729.9, 779.1, 913.8, 1166.4, 1509.2, 1729.5, 1489.9, 1277.1, 1711.2, 1979.0, 1934.4, 2204.2, 2540.2, 3416.6, 4107.7, 4429.6, 4470.4, 4176.8, 3624.2, 3112.9, 1785.9, 1016.0, 949.3, 1292.1, 1638.6, 1897.3, 2031.9, 2203.8, 2187.9, 2103.8, 2078.9, 1992.8, 2008.7, 2610.4, 3575.5, 4801.6, 5896.5, 6609.5, 7029.0, 7028.5, 6710.5, 6117.8, 5569.0, 4322.0, 3023.1, 1770.8, 1739.0, 2406.9, 3250.4, 3200.1, 2841.3, 2700.3, 2394.9, 2422.7, 2374.6, 2629.9, 3276.7, 4005.6, 4502.7, 5263.5, 5842.4, 4990.6, 4707.0, 5740.4, 6879.7, 7110.0, 7249.8, 6560.3, 5845.6, 5825.3, 5377.4, 5107.7, 4543.6, 4237.0, 4917.0, 4678.0, 4352.6, 4356.3, 5287.6, 5173.7, 5596.2, 5205.0, 5353.0]. Please give me a forecast for the next 51 minutes for wind_power. Think about what could be relevant covariates that can help forecast wind_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for wind_power is saved in variable VAL. | external_data/executor_variables/executor_variables_180.pkl | external_data/ground_truth_data/ground_truth_data_180.pkl | external_data/context/context_180.pkl | external_data/constraint/constraint_180.pkl |
181 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 94 minutes. I must monitor the load ramp rate to ensure it does not exceed 61.96854741957093 MW for each time step. Please give me a forecast for the next 58 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 94 minutes. I must monitor the load ramp rate to ensure it does not exceed 61.96854741957093 MW for each time step. The historical load_power data for the past 94 minutes is: [642.36, 649.62, 660.49, 657.53, 656.16, 656.18, 651.02, 653.89, 659.18, 663.71, 668.82, 670.2, 667.38, 664.92, 651.27, 635.65, 624.27, 613.34, 608.96, 601.16, 602.17, 606.22, 613.55, 620.48, 626.51, 629.81, 636.16, 635.45, 628.8, 624.35, 635.06, 636.83, 640.01, 645.98, 657.05, 654.94, 654.74, 648.88, 637.3, 624.13, 610.95, 598.12, 592.27, 593.37, 589.41, 589.55, 593.44, 607.07, 616.41, 624.34, 628.14, 633.82, 635.04, 640.94, 649.52, 651.47, 638.42, 633.37, 628.65, 621.0, 617.08, 608.26, 595.5, 583.74, 572.97, 562.23, 557.29, 555.66, 560.3, 564.22, 568.89, 575.51, 582.89, 592.89, 588.97, 593.76, 593.17, 587.69, 597.99, 594.97, 598.35, 598.23, 600.23, 605.65, 607.54, 605.41, 598.59, 582.95, 571.13, 563.51, 557.07, 548.7, 549.6, 549.82]. Please give me a forecast for the next 58 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_181.pkl | external_data/ground_truth_data/ground_truth_data_181.pkl | external_data/context/context_181.pkl | external_data/constraint/constraint_181.pkl |
182 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 111 minutes. I must monitor the load ramp rate to ensure it does not exceed 1510.9144518812857 MW for each time step. Please give me a forecast for the next 61 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 111 minutes. I must monitor the load ramp rate to ensure it does not exceed 1510.9144518812857 MW for each time step. The historical load_power data for the past 111 minutes is: [15513.37, 15336.81, 15566.81, 16043.84, 16180.39, 16914.7, 18459.48, 20417.48, 22505.07, 24436.48, 25976.8, 26877.65, 27498.5, 27685.41, 27611.32, 27089.28, 25901.83, 24724.42, 23499.23, 21749.29, 20072.71, 18582.49, 17454.44, 16593.93, 16038.0, 15780.34, 15976.09, 16442.83, 16458.62, 17175.6, 18921.31, 21019.78, 23083.62, 24822.42, 26225.6, 27106.41, 27459.42, 27631.3, 27456.57, 26695.96, 25389.49, 24481.52, 23196.18, 21207.01, 19044.33, 17222.13, 16120.06, 15287.27, 14530.03, 14082.88, 13970.28, 13897.76, 13760.51, 14276.1, 15399.17, 16783.09, 18298.41, 19779.06, 21193.29, 22372.98, 23295.32, 23751.94, 23722.02, 22961.07, 21645.61, 20654.93, 19313.08, 17905.29, 16458.57, 15078.14, 14010.83, 13145.11, 12377.89, 11908.83, 11639.63, 11613.94, 11625.17, 12257.86, 13211.04, 14314.88, 15707.12, 16984.91, 18185.05, 19357.1, 20305.43, 21010.51, 21250.85, 20787.03, 19732.18, 18756.25, 17609.15, 16207.51, 14672.12, 13367.52, 12508.47, 11915.22, 11645.99, 11665.65, 12178.89, 13089.73, 13383.91, 13574.81, 13929.63, 14508.82, 15395.57, 16174.37, 17057.71, 18029.07, 18885.28, 19456.11, 19587.94]. Please give me a forecast for the next 61 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_182.pkl | external_data/ground_truth_data/ground_truth_data_182.pkl | external_data/context/context_182.pkl | external_data/constraint/constraint_182.pkl |
183 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 126 minutes. I must monitor the load ramp rate to ensure it does not exceed 567.6339729904873 MW for each time step. Please give me a forecast for the next 17 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 126 minutes. I must monitor the load ramp rate to ensure it does not exceed 567.6339729904873 MW for each time step. The historical load_power data for the past 126 minutes is: [7147.89, 7026.77, 6914.91, 6996.95, 7114.72, 7450.05, 7631.8, 7584.16, 7457.81, 7159.86, 6772.79, 6335.26, 6168.44, 5928.9, 5826.1, 5785.11, 5907.76, 6220.59, 6731.52, 7097.91, 7210.07, 7249.49, 7287.98, 7240.41, 7209.49, 7177.64, 7181.88, 7207.81, 7252.04, 7459.23, 7582.33, 7495.48, 7329.45, 7126.31, 6888.81, 6580.54, 6352.73, 6198.99, 6195.67, 6222.86, 6350.16, 6600.49, 6987.67, 7327.36, 7510.27, 7374.99, 7165.06, 6935.94, 6726.63, 6558.36, 6499.38, 6557.24, 6713.4, 7031.14, 7340.85, 7362.61, 7403.41, 7394.32, 7306.41, 7174.99, 7036.51, 7000.79, 6988.09, 7091.25, 7260.71, 7496.3, 7902.81, 8289.16, 8243.18, 7762.88, 7238.86, 6810.25, 6559.02, 6423.88, 6416.2, 6506.13, 6690.01, 7003.13, 7396.6, 7398.17, 7377.14, 7274.12, 7000.99, 6689.96, 6467.89, 6419.68, 6432.37, 6571.17, 6803.75, 7274.91, 7909.59, 8246.42, 8040.15, 7661.15, 7300.79, 6999.3, 6837.46, 6756.61, 6755.27, 6834.7, 6937.03, 7283.24, 7688.72, 7691.31, 7640.7, 7478.29, 7023.67, 6703.94, 6502.23, 6428.94, 6416.51, 6479.06, 6749.35, 7226.66, 7825.87, 8203.13, 8108.97, 7689.62, 7262.04, 6924.51, 6703.81, 6634.68, 6655.94, 6728.88, 6870.02, 7227.88]. Please give me a forecast for the next 17 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_183.pkl | external_data/ground_truth_data/ground_truth_data_183.pkl | external_data/context/context_183.pkl | external_data/constraint/constraint_183.pkl |
184 | electricity_prediction_single-load_ramp_rate | I have load_power data for the past 198 minutes. I must monitor the load ramp rate to ensure it does not exceed 72.68189743059787 MW for each time step. Please give me a forecast for the next 25 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 198 minutes. I must monitor the load ramp rate to ensure it does not exceed 72.68189743059787 MW for each time step. The historical load_power data for the past 198 minutes is: [1753.73, 1695.35, 1677.63, 1640.62, 1618.34, 1610.47, 1605.88, 1676.3, 1759.26, 1798.05, 1751.62, 1695.53, 1601.76, 1564.5, 1548.52, 1552.74, 1515.0, 1537.74, 1596.22, 1702.1, 1766.68, 1800.57, 1798.96, 1817.39, 1819.15, 1821.09, 1794.89, 1750.46, 1742.25, 1731.52, 1695.66, 1692.78, 1710.79, 1679.29, 1647.29, 1573.12, 1499.67, 1442.56, 1406.42, 1400.68, 1392.07, 1392.06, 1406.66, 1427.79, 1445.35, 1444.51, 1396.57, 1366.84, 1342.34, 1333.67, 1359.56, 1480.21, 1485.4, 1427.19, 1505.69, 1557.94, 1587.15, 1600.69, 1587.74, 1558.34, 1515.36, 1478.39, 1443.6, 1446.18, 1440.88, 1447.04, 1488.91, 1501.65, 1525.04, 1527.62, 1503.24, 1452.35, 1462.1, 1434.78, 1406.23, 1400.94, 1397.02, 1420.61, 1442.88, 1495.18, 1569.03, 1617.22, 1593.05, 1551.48, 1504.86, 1469.71, 1442.65, 1427.18, 1429.23, 1447.91, 1510.03, 1606.06, 1659.49, 1690.05, 1667.14, 1602.68, 1612.57, 1598.26, 1597.96, 1587.9, 1551.7, 1583.58, 1602.28, 1618.61, 1647.95, 1667.97, 1608.45, 1558.48, 1472.45, 1419.79, 1388.77, 1382.0, 1385.79, 1401.55, 1455.79, 1534.72, 1587.71, 1611.55, 1613.61, 1618.43, 1545.95, 1508.15, 1551.45, 1553.8, 1549.48, 1551.97, 1567.23, 1569.34, 1601.79, 1649.49, 1611.65, 1550.93, 1480.56, 1443.98, 1408.4, 1388.27, 1386.77, 1388.49, 1470.16, 1557.9, 1622.89, 1642.04, 1613.02, 1608.86, 1588.31, 1577.05, 1648.05, 1626.21, 1597.29, 1586.56, 1618.38, 1619.13, 1622.64, 1633.61, 1590.46, 1521.9, 1440.51, 1391.27, 1355.17, 1329.88, 1323.99, 1339.98, 1401.34, 1496.55, 1553.28, 1612.44, 1627.39, 1637.67, 1583.91, 1585.39, 1575.85, 1581.84, 1555.1, 1545.4, 1542.34, 1581.97, 1614.91, 1661.3, 1611.16, 1543.18, 1476.56, 1429.92, 1415.18, 1371.2, 1356.02, 1371.26, 1423.56, 1486.8, 1525.0, 1524.17, 1506.19, 1488.35, 1462.83, 1449.5, 1444.16, 1410.43, 1405.58, 1403.58]. Please give me a forecast for the next 25 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_184.pkl | external_data/ground_truth_data/ground_truth_data_184.pkl | external_data/context/context_184.pkl | external_data/constraint/constraint_184.pkl |
185 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 121 minutes. I need to manage the load variability so that it does not exceed 153.52856444908838 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 50 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 121 minutes. I need to manage the load variability so that it does not exceed 153.52856444908838 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 121 minutes is: [344.27, 349.84, 350.0, 360.53, 364.95, 377.63, 401.2, 403.01, 384.78, 368.35, 339.26, 309.82, 282.21, 267.09, 260.52, 252.39, 249.57, 256.26, 268.87, 295.27, 311.8, 305.25, 297.94, 302.72, 303.81, 305.14, 301.5, 303.65, 322.87, 344.15, 365.22, 372.62, 361.93, 346.83, 330.76, 305.55, 288.36, 279.34, 269.0, 264.32, 265.35, 274.08, 294.83, 329.97, 347.64, 341.48, 324.01, 305.67, 305.84, 317.87, 324.26, 329.68, 337.15, 343.72, 367.49, 377.72, 368.78, 356.11, 343.2, 330.27, 312.07, 302.78, 296.03, 287.87, 283.94, 283.56, 290.81, 305.04, 318.26, 333.36, 322.43, 304.93, 295.93, 285.34, 284.16, 298.73, 308.19, 315.34, 338.05, 344.99, 329.31, 324.9, 313.95, 299.69, 281.56, 271.06, 263.48, 255.38, 258.77, 257.94, 263.5, 275.04, 291.3, 300.1, 311.37, 319.71, 320.86, 320.89, 320.34, 317.93, 316.24, 325.43, 344.35, 346.88, 335.23, 326.31, 310.68, 290.78, 269.07, 257.66, 255.55, 253.12, 253.37, 263.36, 282.15, 313.02, 331.09, 331.07, 320.64, 288.7, 290.54]. Please give me a forecast for the next 50 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_185.pkl | external_data/ground_truth_data/ground_truth_data_185.pkl | external_data/context/context_185.pkl | external_data/constraint/constraint_185.pkl |
186 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 133 minutes. I need to manage the load variability so that it does not exceed 2098.1756886326907 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 86 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 133 minutes. I need to manage the load variability so that it does not exceed 2098.1756886326907 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 133 minutes is: [5588.28, 5684.73, 5781.45, 5886.59, 5974.42, 6059.05, 6130.65, 6088.95, 5962.83, 5801.02, 5577.77, 5338.12, 5048.4, 4716.9, 4447.15, 4236.89, 4104.77, 4031.35, 4044.48, 4222.52, 4674.56, 5106.07, 5381.93, 5570.55, 5673.27, 5770.41, 5785.33, 5810.07, 5818.5, 5889.34, 5991.17, 5990.59, 5849.28, 5684.0, 5499.67, 5279.62, 5017.35, 4755.5, 4521.81, 4337.34, 4205.73, 4120.11, 4086.23, 4134.38, 4287.54, 4479.9, 4720.3, 4938.33, 5145.4, 5312.93, 5455.95, 5544.77, 5621.87, 5702.19, 5738.6, 5751.37, 5793.79, 5758.68, 5606.88, 5376.43, 5077.63, 4786.02, 4539.65, 4328.36, 4155.4, 4028.55, 3949.55, 3941.59, 4027.16, 4146.0, 4314.85, 4511.61, 4664.44, 4813.77, 4899.81, 4947.25, 4975.42, 4979.99, 5008.27, 5103.1, 5174.21, 5118.85, 5020.99, 4866.46, 4658.63, 4396.45, 4177.69, 4012.77, 3919.53, 3857.32, 3892.89, 4108.56, 4588.54, 5070.63, 5357.56, 5546.8, 5609.34, 5647.16, 5668.42, 5660.98, 5652.62, 5664.06, 5688.62, 5713.14, 5648.55, 5500.28, 5327.87, 5121.9, 4838.0, 4512.17, 4238.98, 4051.83, 3938.22, 3883.0, 3917.08, 4130.04, 4596.76, 5052.04, 5307.3, 5395.58, 5434.85, 5471.69, 5449.02, 5479.89, 5498.54, 5537.99, 5578.84, 5608.8, 5559.24, 5453.2, 5291.91, 5109.72, 4828.85]. Please give me a forecast for the next 86 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_186.pkl | external_data/ground_truth_data/ground_truth_data_186.pkl | external_data/context/context_186.pkl | external_data/constraint/constraint_186.pkl |
187 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 99 minutes. I need to manage the load variability so that it does not exceed 2404.653416715156 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 99 minutes. I need to manage the load variability so that it does not exceed 2404.653416715156 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 99 minutes is: [3708.62, 3918.23, 3985.53, 4118.83, 4202.09, 4272.39, 4325.53, 4148.13, 4046.97, 4096.77, 4003.08, 3897.56, 3700.9, 3460.62, 3209.37, 3074.49, 2968.05, 2924.56, 2932.45, 3043.67, 3284.09, 3319.08, 3353.02, 3525.59, 3718.44, 4022.85, 4291.32, 4498.1, 4648.88, 4896.65, 4892.0, 4768.23, 4570.07, 4493.97, 4342.41, 4141.14, 3869.77, 3567.9, 3364.34, 3230.06, 3106.96, 3057.94, 2994.76, 3161.68, 3444.27, 3463.12, 3521.79, 3647.3, 3847.5, 4183.63, 4461.36, 4674.07, 4808.33, 4851.5, 4901.17, 4643.73, 4493.3, 4551.71, 4445.97, 4278.78, 4028.31, 3790.75, 3502.97, 3375.49, 3238.16, 3179.11, 3142.57, 3190.36, 3507.31, 3505.22, 3511.25, 3834.29, 4067.96, 4333.6, 4594.97, 4826.15, 5046.91, 5226.55, 5251.92, 4991.53, 4877.16, 4776.07, 4588.16, 4423.88, 4077.3, 3703.53, 3452.95, 3296.18, 3187.93, 3066.21, 3060.9, 3153.69, 3415.29, 3467.75, 3498.93, 3662.72, 3884.67, 4133.44, 4353.44]. Please give me a forecast for the next 23 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_187.pkl | external_data/ground_truth_data/ground_truth_data_187.pkl | external_data/context/context_187.pkl | external_data/constraint/constraint_187.pkl |
188 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 154 minutes. I need to manage the load variability so that it does not exceed 343.32980312966833 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 28 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 154 minutes. I need to manage the load variability so that it does not exceed 343.32980312966833 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 154 minutes is: [929.72, 954.37, 982.62, 970.57, 920.04, 852.02, 784.2, 735.37, 701.24, 673.97, 656.85, 661.92, 693.63, 749.12, 788.28, 807.81, 819.19, 848.74, 840.8, 806.25, 803.73, 813.78, 846.97, 870.11, 923.98, 953.75, 981.69, 989.23, 941.5, 848.04, 778.04, 729.45, 705.92, 697.48, 683.8, 703.78, 737.22, 774.2, 814.98, 852.43, 850.02, 851.16, 853.94, 852.91, 860.3, 847.93, 867.15, 875.94, 896.26, 885.86, 875.11, 876.13, 831.57, 763.39, 707.01, 664.07, 638.34, 618.11, 613.14, 623.86, 657.97, 707.91, 751.86, 761.64, 738.43, 718.77, 714.14, 730.98, 752.29, 761.71, 774.91, 808.47, 866.74, 908.11, 935.01, 927.19, 898.21, 842.89, 767.68, 711.52, 676.59, 655.1, 645.28, 649.93, 671.1, 692.86, 714.93, 737.33, 745.71, 770.76, 790.69, 821.96, 864.71, 856.99, 874.66, 942.73, 994.67, 995.63, 1021.58, 1004.69, 971.21, 905.69, 833.43, 780.11, 745.91, 716.12, 700.34, 693.18, 690.41, 696.63, 722.18, 742.19, 735.29, 771.28, 806.04, 829.08, 861.27, 887.0, 938.49, 983.18, 1000.41, 997.69, 988.78, 980.05, 958.66, 920.37, 849.97, 786.07, 748.26, 722.05, 700.53, 692.43, 694.85, 702.44, 736.72, 778.79, 816.44, 847.1, 864.32, 871.01, 854.91, 869.03, 886.04, 927.52, 999.24, 1008.71, 1020.65, 1005.4, 978.7, 914.11, 836.65, 779.2, 745.09, 719.85]. Please give me a forecast for the next 28 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_188.pkl | external_data/ground_truth_data/ground_truth_data_188.pkl | external_data/context/context_188.pkl | external_data/constraint/constraint_188.pkl |
189 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 121 minutes. I need to manage the load variability so that it does not exceed 4278.117152516438 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 57 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 121 minutes. I need to manage the load variability so that it does not exceed 4278.117152516438 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 121 minutes is: [6870.23, 6762.86, 6340.8, 5884.66, 5543.4, 5344.36, 5245.41, 5230.86, 5351.29, 5683.48, 6289.93, 6626.48, 6701.47, 6718.26, 6644.77, 6528.46, 6501.82, 6560.17, 6634.4, 6737.97, 6882.67, 7082.47, 7123.41, 7167.83, 7262.84, 7135.88, 6683.8, 6164.59, 5726.33, 5532.18, 5396.33, 5336.27, 5409.19, 5715.31, 6275.92, 6561.09, 6668.89, 6785.98, 6869.57, 6958.97, 7082.54, 7140.46, 7165.83, 7266.26, 7469.94, 7664.83, 7772.71, 7818.15, 7873.72, 7741.68, 7281.88, 6758.76, 6330.27, 6055.84, 5887.29, 5790.79, 5879.42, 6144.84, 6606.6, 6906.78, 7057.69, 7236.83, 7473.71, 7728.73, 7935.38, 8311.97, 8687.24, 9006.39, 9293.22, 9516.03, 9466.2, 9253.36, 9134.32, 8768.73, 8103.05, 7330.09, 6623.06, 6203.49, 5898.99, 5717.23, 5729.45, 5951.48, 6443.98, 6658.85, 6772.18, 6878.79, 7010.14, 7154.7, 7334.56, 7577.54, 7820.79, 8196.66, 8581.89, 8863.55, 8902.59, 8620.93, 8328.43, 7950.39, 7380.91, 6725.54, 6089.14, 5740.14, 5536.59, 5456.23, 5462.61, 5720.65, 6165.75, 6467.25, 6682.92, 6918.89, 7137.81, 7402.38, 7694.95, 8011.45, 8409.69, 8801.25, 9123.95, 9023.35, 8510.97, 7948.55, 7630.07]. Please give me a forecast for the next 57 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_189.pkl | external_data/ground_truth_data/ground_truth_data_189.pkl | external_data/context/context_189.pkl | external_data/constraint/constraint_189.pkl |
190 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 179 minutes. I need to manage the load variability so that it does not exceed 131.25976996612877 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 52 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 179 minutes. I need to manage the load variability so that it does not exceed 131.25976996612877 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 179 minutes is: [644.53, 648.35, 660.87, 663.64, 650.82, 642.51, 631.61, 622.61, 610.48, 599.92, 595.46, 590.6, 610.26, 605.37, 633.06, 631.01, 637.13, 633.37, 631.11, 634.21, 639.59, 635.23, 632.82, 634.05, 641.54, 659.97, 699.99, 699.93, 684.89, 667.95, 658.41, 628.11, 619.48, 613.44, 607.64, 625.03, 630.12, 638.06, 638.38, 644.62, 659.54, 651.42, 632.53, 615.25, 603.32, 610.37, 609.51, 606.68, 613.45, 627.03, 636.18, 651.44, 647.83, 642.49, 632.53, 632.39, 628.19, 630.43, 628.03, 630.88, 635.09, 647.04, 633.77, 639.81, 652.59, 630.95, 619.83, 614.92, 607.96, 605.77, 604.14, 604.47, 599.63, 618.68, 620.01, 628.1, 648.17, 656.7, 639.37, 617.11, 610.38, 603.6, 615.62, 608.92, 602.92, 613.47, 614.53, 639.88, 648.11, 628.41, 605.51, 600.2, 593.29, 589.85, 594.9, 594.35, 592.83, 611.2, 623.25, 634.69, 607.45, 591.34, 590.62, 610.16, 606.24, 599.86, 599.76, 610.61, 616.21, 612.34, 615.45, 620.23, 621.12, 606.16, 606.48, 598.96, 588.45, 583.33, 578.89, 576.15, 562.38, 570.77, 585.13, 605.22, 599.88, 583.35, 578.74, 580.38, 577.56, 582.2, 582.9, 583.93, 609.35, 612.02, 617.8, 623.5, 622.89, 615.51, 600.39, 592.28, 582.03, 567.77, 558.9, 556.71, 553.96, 572.39, 567.7, 570.88, 569.92, 570.44, 573.61, 569.46, 572.97, 563.24, 565.43, 569.31, 577.44, 590.48, 591.01, 594.69, 579.42, 437.38, 441.37, 491.89, 526.87, 547.26, 556.9, 561.88, 560.41, 559.23, 562.92, 569.23, 580.45, 583.97, 581.0, 580.84, 575.15, 566.69, 571.94]. Please give me a forecast for the next 52 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_190.pkl | external_data/ground_truth_data/ground_truth_data_190.pkl | external_data/context/context_190.pkl | external_data/constraint/constraint_190.pkl |
191 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 187 minutes. I need to manage the load variability so that it does not exceed 79.74844016621668 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 63 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 187 minutes. I need to manage the load variability so that it does not exceed 79.74844016621668 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 187 minutes is: [589.63, 601.22, 558.38, 582.66, 611.87, 605.73, 601.21, 602.41, 594.55, 602.56, 601.92, 611.93, 614.04, 620.25, 613.16, 597.14, 579.71, 566.93, 561.0, 562.88, 567.48, 561.22, 565.17, 575.78, 596.13, 604.62, 522.43, 599.38, 603.42, 605.97, 607.74, 610.37, 605.9, 608.38, 608.27, 615.47, 620.88, 627.05, 622.18, 605.11, 588.17, 575.67, 569.97, 564.0, 560.91, 559.23, 562.39, 573.73, 597.25, 609.93, 598.22, 590.02, 583.28, 579.33, 578.76, 580.34, 577.75, 585.34, 594.18, 602.33, 614.87, 622.43, 617.86, 605.68, 593.82, 577.43, 571.96, 568.22, 566.94, 563.77, 563.71, 567.86, 574.61, 584.11, 596.97, 598.8, 588.38, 580.5, 572.27, 563.15, 562.64, 562.61, 574.43, 586.98, 596.31, 610.37, 618.58, 606.21, 591.87, 565.75, 576.25, 557.02, 552.37, 551.88, 551.03, 555.0, 581.43, 585.71, 589.32, 568.74, 569.77, 572.23, 576.94, 587.86, 599.28, 602.39, 612.23, 619.78, 633.05, 638.01, 638.53, 630.86, 611.82, 601.66, 592.16, 588.52, 569.19, 568.99, 571.87, 584.88, 605.17, 626.87, 620.34, 617.2, 615.19, 620.86, 622.86, 626.03, 620.13, 621.41, 625.96, 631.28, 638.24, 640.35, 633.68, 616.22, 613.78, 593.98, 592.09, 579.56, 580.91, 585.77, 591.05, 587.24, 608.66, 623.8, 626.35, 622.29, 623.72, 624.41, 624.91, 621.43, 629.82, 640.49, 632.57, 649.9, 647.64, 634.47, 639.88, 621.63, 599.69, 599.27, 587.89, 586.27, 582.23, 583.36, 582.45, 600.89, 619.56, 615.64, 604.87, 601.89, 583.25, 583.54, 582.68, 591.56, 579.66, 584.87, 600.56, 616.36, 631.45, 636.95, 627.77, 610.46, 598.41, 589.59, 582.79]. Please give me a forecast for the next 63 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_191.pkl | external_data/ground_truth_data/ground_truth_data_191.pkl | external_data/context/context_191.pkl | external_data/constraint/constraint_191.pkl |
192 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 151 minutes. I need to manage the load variability so that it does not exceed 2693.4260185329535 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 84 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 151 minutes. I need to manage the load variability so that it does not exceed 2693.4260185329535 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 151 minutes is: [3525.54, 3845.01, 4154.83, 4438.82, 4763.85, 4967.11, 5102.99, 5183.16, 5205.53, 5205.21, 4924.48, 4748.36, 4587.43, 4260.7, 3914.0, 3594.06, 3379.45, 3294.31, 3186.83, 3144.14, 3310.01, 3455.45, 3437.73, 3652.1, 3979.35, 4240.94, 4561.37, 4852.1, 5071.19, 5252.72, 5393.88, 5514.08, 5600.07, 5418.63, 5222.08, 5044.56, 4897.49, 4547.2, 4191.98, 3922.69, 3656.86, 3468.32, 3386.18, 3300.84, 3388.72, 3545.97, 3639.95, 3691.46, 3849.8, 4115.99, 4412.72, 4709.32, 4983.12, 5200.76, 5347.4, 5386.95, 5305.05, 5171.25, 4985.3, 4852.05, 4689.63, 4447.03, 4147.09, 3777.49, 3606.86, 3420.01, 3364.77, 3327.83, 3409.92, 3557.7, 3623.11, 3800.91, 4017.75, 4388.05, 4665.13, 4943.52, 5099.09, 5213.76, 5305.25, 5479.58, 5394.16, 5181.11, 4912.2, 4833.08, 4598.91, 4310.19, 3998.51, 3713.06, 3481.18, 3320.73, 3207.2, 3177.91, 3221.66, 3348.63, 3407.0, 3595.69, 3895.49, 4166.72, 4422.93, 4671.12, 4911.53, 5123.32, 5258.56, 5321.06, 5237.08, 5097.24, 4901.53, 4693.8, 4483.64, 4290.41, 4036.67, 3762.77, 3555.88, 3424.34, 3322.62, 3247.58, 3251.58, 3205.79, 3278.6, 3556.41, 3858.59, 4151.1, 4382.27, 4590.23, 4894.05, 5028.76, 5037.4, 5055.97, 4953.27, 4805.85, 4594.88, 4445.25, 4295.17, 4071.0, 3884.74, 3677.15, 3474.83, 3340.57, 3211.0, 3161.63, 3145.4, 3079.63, 3128.83, 3232.38, 3266.44, 3305.4, 3566.76, 3738.34, 3864.79, 4057.14, 4244.11]. Please give me a forecast for the next 84 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_192.pkl | external_data/ground_truth_data/ground_truth_data_192.pkl | external_data/context/context_192.pkl | external_data/constraint/constraint_192.pkl |
193 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 126 minutes. I need to manage the load variability so that it does not exceed 746.577204318205 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 83 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 126 minutes. I need to manage the load variability so that it does not exceed 746.577204318205 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 126 minutes is: [1827.63, 1925.42, 1968.17, 2012.98, 2043.66, 2036.63, 1997.97, 1905.94, 1799.95, 1737.05, 1668.3, 1555.25, 1471.46, 1408.47, 1365.51, 1333.2, 1310.82, 1314.01, 1335.67, 1341.65, 1381.99, 1462.56, 1572.49, 1705.88, 1827.27, 1936.33, 2002.81, 2047.98, 2091.17, 2083.79, 2064.78, 1994.62, 1933.15, 1831.34, 1689.8, 1571.41, 1449.21, 1376.13, 1328.57, 1293.02, 1272.9, 1271.75, 1305.62, 1310.7, 1355.65, 1467.85, 1597.57, 1731.54, 1854.34, 1961.46, 2037.78, 2054.49, 2065.67, 2049.95, 2028.97, 1973.33, 1893.55, 1788.58, 1670.78, 1563.83, 1479.12, 1421.44, 1367.21, 1315.14, 1274.72, 1249.79, 1251.41, 1286.09, 1325.8, 1436.2, 1563.37, 1683.55, 1811.7, 1915.14, 1995.8, 2062.32, 2080.71, 2074.35, 2032.79, 1938.49, 1829.36, 1695.3, 1596.66, 1498.29, 1408.98, 1373.81, 1319.75, 1270.1, 1231.01, 1214.3, 1207.93, 1201.62, 1241.33, 1314.7, 1402.43, 1505.95, 1646.87, 1695.79, 1783.43, 1860.35, 1898.97, 1886.35, 1844.28, 1772.86, 1709.19, 1619.66, 1510.51, 1406.02, 1306.75, 1246.44, 1236.39, 1212.47, 1200.27, 1222.01, 1274.83, 1278.83, 1292.67, 1317.92, 1369.56, 1451.21, 1541.44, 1623.87, 1717.76, 1788.08, 1804.3, 1776.08]. Please give me a forecast for the next 83 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_193.pkl | external_data/ground_truth_data/ground_truth_data_193.pkl | external_data/context/context_193.pkl | external_data/constraint/constraint_193.pkl |
194 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 93 minutes. I need to manage the load variability so that it does not exceed 331.1887143838759 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 77 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 93 minutes. I need to manage the load variability so that it does not exceed 331.1887143838759 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 93 minutes is: [269.96, 255.0, 248.71, 244.42, 253.26, 258.63, 276.67, 293.14, 301.45, 321.02, 334.9, 350.3, 369.15, 378.61, 394.99, 409.63, 430.69, 428.2, 415.22, 395.51, 375.46, 347.72, 311.06, 280.53, 261.19, 243.4, 232.41, 231.45, 242.58, 256.89, 280.97, 302.85, 315.11, 337.05, 358.48, 379.35, 400.86, 424.39, 440.18, 449.89, 446.06, 436.57, 421.88, 401.45, 377.59, 344.04, 308.22, 281.36, 259.97, 245.72, 235.96, 231.5, 230.06, 235.48, 253.81, 271.58, 295.16, 318.47, 335.66, 364.19, 392.35, 398.38, 405.93, 410.6, 409.03, 397.43, 382.6, 369.62, 355.19, 336.5, 309.45, 282.32, 269.32, 258.88, 249.9, 246.03, 246.73, 249.91, 266.75, 285.83, 305.04, 322.81, 343.3, 340.57, 376.08, 406.68, 432.19, 451.31, 477.96, 475.0, 458.56, 449.26, 432.7]. Please give me a forecast for the next 77 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_194.pkl | external_data/ground_truth_data/ground_truth_data_194.pkl | external_data/context/context_194.pkl | external_data/constraint/constraint_194.pkl |
195 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 90 minutes. I need to manage the load variability so that it does not exceed 316.0688006346039 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 41 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 90 minutes. I need to manage the load variability so that it does not exceed 316.0688006346039 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 90 minutes is: [1081.43, 1082.69, 1030.52, 994.71, 991.88, 1000.93, 1040.15, 1094.67, 1135.98, 1126.0, 1114.47, 1102.33, 1086.92, 1072.15, 1080.91, 1088.24, 1095.59, 1101.85, 1107.42, 1134.8, 1126.48, 1108.07, 1083.94, 1042.09, 1002.57, 983.96, 982.9, 980.35, 994.34, 1029.4, 1090.38, 1196.53, 1246.21, 1207.86, 1157.8, 1124.28, 1102.83, 1091.17, 1093.0, 1086.17, 1085.29, 1109.92, 1115.76, 1150.66, 1147.16, 1125.73, 1118.21, 1050.82, 995.14, 971.78, 958.43, 955.93, 955.53, 970.76, 1015.1, 1102.12, 1155.98, 1142.0, 1129.44, 1135.29, 1128.72, 1118.47, 1122.66, 1112.45, 1112.08, 1125.27, 1134.44, 1183.46, 1192.69, 1186.34, 1165.7, 1127.23, 1091.75, 1081.48, 1072.58, 1083.03, 1099.96, 1131.97, 1197.85, 1313.3, 1359.09, 1316.33, 1252.67, 1209.85, 1168.4, 1132.38, 1112.81, 1097.96, 1086.45, 1083.29]. Please give me a forecast for the next 41 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_195.pkl | external_data/ground_truth_data/ground_truth_data_195.pkl | external_data/context/context_195.pkl | external_data/constraint/constraint_195.pkl |
196 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 56 minutes. I need to manage the load variability so that it does not exceed 922.2259174740853 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 53 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 56 minutes. I need to manage the load variability so that it does not exceed 922.2259174740853 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 56 minutes is: [1791.06, 1716.15, 1644.68, 1530.79, 1410.84, 1314.71, 1252.96, 1208.3, 1181.06, 1160.14, 1161.48, 1201.93, 1220.34, 1246.68, 1341.28, 1455.23, 1599.89, 1723.23, 1828.58, 1901.7, 1996.36, 2044.99, 2034.97, 1995.78, 1915.38, 1843.33, 1734.45, 1592.29, 1474.93, 1409.12, 1339.28, 1289.78, 1263.34, 1238.82, 1234.16, 1257.84, 1256.34, 1293.95, 1382.72, 1498.16, 1645.39, 1786.66, 1905.79, 2007.47, 2044.8, 2049.23, 2029.23, 2015.21, 1940.15, 1867.59, 1761.2, 1646.77, 1549.91, 1446.32, 1382.25, 1326.42]. Please give me a forecast for the next 53 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_196.pkl | external_data/ground_truth_data/ground_truth_data_196.pkl | external_data/context/context_196.pkl | external_data/constraint/constraint_196.pkl |
197 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 191 minutes. I need to manage the load variability so that it does not exceed 4161.219897697502 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 13 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 191 minutes. I need to manage the load variability so that it does not exceed 4161.219897697502 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 191 minutes is: [17434.49, 18353.91, 18634.29, 18587.66, 18174.52, 17670.42, 17171.45, 16365.41, 15972.79, 15815.42, 15739.14, 15848.47, 16278.38, 17351.82, 18786.27, 19096.82, 18775.15, 18337.79, 18131.21, 17778.58, 17584.72, 17380.65, 17296.95, 17452.98, 18056.93, 19075.28, 19615.32, 19854.97, 19918.05, 19426.19, 18641.27, 18094.9, 17743.85, 17733.4, 17833.37, 18012.04, 18577.54, 19872.54, 21488.88, 21621.74, 20916.29, 19941.69, 18945.51, 18331.9, 17731.0, 17060.3, 16985.72, 17401.68, 18221.76, 19255.6, 19807.69, 20016.19, 20011.05, 19415.31, 18688.31, 17888.28, 17586.2, 17548.59, 17646.13, 17806.6, 18499.16, 19778.61, 21106.56, 21304.3, 20516.8, 19786.86, 19041.07, 18262.66, 17735.26, 17410.07, 17375.83, 17482.46, 18366.98, 19792.71, 20659.16, 20871.31, 21000.18, 20739.46, 20091.7, 19463.22, 19274.69, 19236.68, 19355.06, 19556.16, 20257.81, 21463.09, 23024.38, 23002.22, 21897.2, 20850.81, 19649.66, 18778.82, 18053.8, 17659.13, 17366.33, 17384.98, 17972.94, 19227.93, 19704.88, 19896.59, 19874.17, 19357.85, 18528.15, 17744.69, 17346.93, 16979.65, 16873.43, 16775.83, 17030.28, 17902.61, 18876.31, 19057.65, 18825.8, 18643.07, 18423.12, 18140.37, 17872.87, 17808.91, 17609.49, 17575.53, 17975.28, 18589.67, 18507.1, 18278.05, 17865.29, 17427.62, 17005.64, 16356.51, 15806.98, 15322.2, 15118.17, 14991.15, 15062.62, 15289.56, 15720.26, 16062.55, 16533.84, 17058.37, 17391.33, 17672.98, 17830.37, 17905.14, 17990.73, 18073.47, 18178.45, 18713.16, 18613.28, 18360.84, 18019.25, 17655.21, 17097.62, 16433.21, 15906.34, 15599.16, 15320.69, 15339.63, 15450.71, 15931.45, 16591.1, 17404.04, 17987.59, 18183.2, 17958.23, 17707.15, 17381.35, 17099.17, 16930.47, 17139.11, 18085.29, 19673.95, 20444.11, 20854.34, 21030.92, 20755.2, 20086.96, 19655.89, 19463.91, 19556.54, 19739.76, 20179.73, 21007.0, 22481.38, 24284.18, 24663.54, 23460.73, 22100.41, 20975.65, 19935.52, 19016.55, 18589.15, 18089.45]. Please give me a forecast for the next 13 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_197.pkl | external_data/ground_truth_data/ground_truth_data_197.pkl | external_data/context/context_197.pkl | external_data/constraint/constraint_197.pkl |
198 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 150 minutes. I need to manage the load variability so that it does not exceed 12872.899758677348 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 77 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 150 minutes. I need to manage the load variability so that it does not exceed 12872.899758677348 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 150 minutes is: [12047.78, 11756.5, 11775.38, 12185.68, 12835.15, 13179.2, 13498.07, 13848.87, 14211.57, 14529.38, 14963.62, 15497.41, 15800.72, 15930.2, 16074.11, 16075.9, 15981.47, 15809.3, 15703.79, 15607.4, 14814.59, 13720.82, 12883.13, 12255.92, 11916.3, 11700.35, 11828.48, 12430.62, 13195.13, 13736.69, 14240.82, 14863.22, 15689.27, 16569.74, 17480.74, 18553.62, 19530.36, 20373.34, 20993.59, 21069.31, 20641.31, 19811.13, 18974.58, 18582.23, 17412.92, 15945.77, 14703.12, 13730.56, 13112.72, 12670.62, 12654.67, 12970.06, 13539.82, 14093.84, 15065.81, 16312.74, 17813.44, 19228.02, 20474.72, 21350.84, 22128.62, 22612.64, 22837.48, 22879.17, 22702.37, 21946.18, 21121.93, 20556.73, 18986.63, 16874.48, 15465.09, 14317.79, 13519.88, 13006.76, 12678.59, 12798.37, 13091.65, 13542.83, 14430.69, 15562.1, 16606.24, 17813.03, 19295.15, 20827.54, 21858.87, 22194.71, 22527.38, 22407.55, 21722.85, 20315.64, 19317.61, 18615.08, 17351.95, 16046.94, 14803.92, 13787.0, 13037.65, 12473.97, 12169.23, 12131.0, 12167.53, 12441.78, 13458.74, 14819.37, 15880.22, 17260.59, 18771.57, 20022.52, 21016.66, 21836.12, 22361.7, 22355.55, 21650.44, 20782.01, 20146.71, 19564.51, 18459.94, 17189.57, 15879.05, 15008.23, 14421.94, 13856.87, 13406.58, 12943.19, 12709.22, 12681.4, 13373.15, 14386.51, 15468.28, 16647.82, 17674.57, 18740.9, 19770.27, 20535.07, 21084.72, 21420.52, 21227.89, 20602.51, 19640.8, 19046.57, 17669.08, 16222.56, 14897.45, 13861.41, 13136.71, 12712.42, 12638.94, 12979.2, 13318.11, 14016.25]. Please give me a forecast for the next 77 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_198.pkl | external_data/ground_truth_data/ground_truth_data_198.pkl | external_data/context/context_198.pkl | external_data/constraint/constraint_198.pkl |
199 | electricity_prediction_single-load_variability_limit | I have load_power data for the past 152 minutes. I need to manage the load variability so that it does not exceed 101.058753227659 MW over the complete time period (i.e the maximum change in load over the entire period). Please give me a forecast for the next 84 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL.
Requirements:
- Store your output in the variable called `predictions`, make sure to consider predictions to have the right shape according to the question's output requirements if it's supposed to be an array, there is no need to consider shapes if you only need to return a numerical value.
- Do not customly define/generate/overwrite the available variables, assume that the variables are already defined and available.
data note: VAL values are usually stored in either a dataframe or numpy values.
You should enclose your python code in <execute> </execute> tag and do not overwrite available variables that store the data. Do not use any other tags like ```python```. | I have load_power data for the past 152 minutes. I need to manage the load variability so that it does not exceed 101.058753227659 MW over the complete time period (i.e the maximum change in load over the entire period). The historical load_power data for the past 152 minutes is: [624.6, 623.36, 622.76, 624.21, 629.56, 647.88, 679.17, 690.64, 678.75, 672.62, 657.29, 651.54, 643.48, 626.45, 625.07, 633.75, 632.83, 666.92, 661.0, 664.23, 669.98, 675.16, 657.33, 633.82, 607.13, 604.69, 603.69, 604.55, 608.28, 630.63, 686.55, 682.41, 602.89, 580.09, 547.38, 535.76, 534.42, 530.37, 522.67, 561.72, 600.68, 633.5, 648.99, 664.61, 669.08, 649.14, 610.54, 598.49, 593.07, 588.9, 589.95, 600.29, 613.52, 628.28, 659.58, 663.24, 655.39, 632.93, 602.05, 590.79, 578.06, 571.61, 582.33, 585.28, 592.43, 620.85, 630.83, 624.02, 628.74, 620.84, 592.85, 592.46, 592.74, 588.75, 584.05, 581.99, 584.85, 592.28, 598.64, 605.63, 621.22, 616.71, 609.43, 615.01, 621.64, 619.76, 615.62, 614.64, 622.91, 621.85, 621.64, 623.05, 620.22, 623.97, 603.11, 611.67, 604.99, 600.36, 598.13, 584.43, 595.24, 587.84, 588.11, 610.57, 617.14, 621.08, 623.87, 630.6, 630.11, 629.79, 614.16, 619.76, 641.53, 652.09, 646.97, 658.52, 655.27, 645.85, 632.04, 620.45, 608.26, 594.66, 586.2, 589.34, 589.48, 594.97, 619.56, 629.82, 642.9, 654.82, 656.72, 654.66, 664.58, 636.03, 632.2, 642.37, 640.26, 644.34, 651.89, 664.03, 663.54, 651.31, 631.98, 608.64, 611.07, 613.4, 613.39, 609.58, 616.76, 631.78, 654.08, 648.79]. Please give me a forecast for the next 84 minutes for load_power. Think about what could be relevant covariates that can help forecast load_power. Your goal is to make the most accurate forecast as possible, refine prediction result based on the constraint previously described, and return the result as a 1D numpy array. The historical data for load_power is saved in variable VAL. | external_data/executor_variables/executor_variables_199.pkl | external_data/ground_truth_data/ground_truth_data_199.pkl | external_data/context/context_199.pkl | external_data/constraint/constraint_199.pkl |
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