Replication Data for: A gradient boosting approach for optimal selection of bidding strategies: Simple model - Original variables
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Access to an increasing amount of data opens for the application of machine learningmodels to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume.  The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange.  The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["bidding strategies","gradient boosting","hydropower","neural network","reservoir hydro"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Access to an increasing amount of data opens for the application of machine learningmodels to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume.  The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange.  The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Access to an increasing amount of data opens for the application of machine learningmodels to predict the best combination of models and strategies for bidding of hydro power in a de-regulated market for any given day. This data-set describe the historical performance-gap of two given bidding strategies over several years (2016-2018). Data from two different bidding strategies are presented in the data-set. The first is bidding the expected volume.  The expected volumes are found by deterministic optimization against forecasted price and inflow using the SHOP software, and are submitted as fixed hourly bids to the Nord Pool power exchange.  The second strategy is stochastic bidding. The stochastic model is based on the deterministic method, but allows for a stochastic representation of inflow to the reservoir and day-ahead market 
bidding strategies; gradient boosting; hydropower; neural network; reservoir hydro