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Electricity demand forecasting and risk management using Gaussian process model with error propagation

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  • Kuangyu Wen
  • Wenbin Wu
  • Ximing Wu

Abstract

Electricity demand forecasting plays a vital role in power system planning, operation, transmission design, and financial risk management. In this study, we employ a Gaussian Process model to estimate the probability distributions of short‐term electricity demand. To account for input uncertainty in multi‐period ahead forecast, we adopt an error propagation procedure that improves upon the naive recursive forecasting. This method is shown to perform well in terms of multi‐step ahead point and probabilistic forecasts. We illustrate the usefulness of the suggested estimator with an application to short term demand forecasting of Texas electricity market.

Suggested Citation

  • Kuangyu Wen & Wenbin Wu & Ximing Wu, 2023. "Electricity demand forecasting and risk management using Gaussian process model with error propagation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 957-969, July.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:4:p:957-969
    DOI: 10.1002/for.2925
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    References listed on IDEAS

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