A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series

Abstract

Day-ahead electricity market is crucial for ensuring the balance of electricity supply and demand. Its electricity price serves as a key guide for market participants and power dispatching agencies, making accurate short-term price forecasting essential. In this paper, a hybrid short-term electricity price forecasting method considering the long-term dependence of wavelet transform (WT) series is proposed. Different from the existing studies that do not distinguish the characteristics of WT series, this paper uses Hurst exponent and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to analyze the long-term dependence of subseries, and chooses seasonal autoregressive integrated moving average (SARIMA) or long short-term memory (LSTM) method to construct a hybrid forecasting model according to the characteristics of each subseries. Validation using real electricity prices from the NYISO and PJM markets shows that, compared to methods that do not consider long-term dependencies of subseries, the proposed method can improve prediction accuracy by up to 50.98 % and stability by up to 68.09 %.