Local Cold Load Pick-up Estimation Using Customer Energy Consumption Measurements
<|reference_start|>Local Cold Load Pick-up Estimation Using Customer Energy Consumption Measurements: Thermostatically-controlled loads have a significant impact on electricity demand after service is restored following an outage, a phenomenon known as cold load pick-up (CLPU). Active management of CLPU is becoming an essential tool for distribution system operators who seek to defer network upgrades and speed up post-outage customer restoration. One key functionality needed for actively managing CLPU is its forecast at various scales. The widespread deployment of smart metering devices is also opening up new opportunities for data-driven load modeling and forecast. In this paper, we propose an approach for customer-side estimation of CLPU using time-stamped local load measurements. The proposed method uses Auto-Regressive Integrated Moving Average (ARIMA) modeling for short-term foregone energy consumption forecast during an outage. Forecasts are made on an hourly basis to estimate the energy to potentially recover after outages lasting up to several hours. Moreover, to account for changing customer behavior and weather, the model order is adjusted dynamically. Simulation results based on actual smart meter measurements are presented for 50 residential customers over the duration of one year. These results are validated using physical modeling of residential loads and are shown to match well the ARIMA-based forecasts. Additionally, accuracy and execution speed has been compared with other state-of-the-art approaches for time-series forecasting including Long Short Term Memory Network (LSTM) and Holt-Winters Exponential Smoothing (HWES). ARIMA-based forecast is found to offer superior performance both in terms of accuracy and computation speed.<|reference_end|>
@article{bajic2024local,
    title={Local Cold Load Pick-up Estimation Using Customer Energy Consumption
  Measurements},
    author={Sanja Bajic and Franc{c}ois Bouffard and Hannah Michalska and G'eza
  Jo'os},
    journal={arXiv preprint arXiv:2408.07754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07754},
    primaryClass={eess.SY cs.SY}
}