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

Kuangyu Wen, Wenbin Wu and Ximing Wu

Journal of Forecasting, 2023, vol. 42, issue 4, 957-969

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.

Date: 2023
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https://doi.org/10.1002/for.2925

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:4:p:957-969

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