A Comparative Study of Electricity Sales Forecasting Models Based on Different Feature Decomposition Methods

Shichong Chen, Yushu Zhang, Xiaoteng Ma, Xu Yang, Junyi Shi () and Haoyang Ji ()
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Shichong Chen: State Grid Information & Telecommunication Center (Big Data Center), Beijing 100033, China
Yushu Zhang: State Grid Information & Telecommunication Center (Big Data Center), Beijing 100033, China
Xiaoteng Ma: State Grid Information & Telecommunication Center (Big Data Center), Beijing 100033, China
Xu Yang: Data Asset Management Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
Junyi Shi: Data Asset Management Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
Haoyang Ji: Data Asset Management Research Center, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2025, vol. 18, issue 20, 1-23

Abstract: Accurate forecasting of electricity sales holds significant practical importance. On the one hand, it helps to implement and achieve the annual goals of power companies, and on the other hand, it helps to control the balance of enterprise profits. This study was conducted in China using data from the State Grid Corporation (Henan, Fujian, and national data) from the Wind database. Based on collected data such as electricity sales, this study addresses the limitations of the existing literature, which mostly employs a single feature decomposition method for forecasting. We simultaneously apply three decomposition techniques—seasonal adjustment decomposition (X13), empirical mode decomposition (EMD), and discrete wavelet transform (DWT)—to decompose electricity sales into multiple components. Subsequently, we model each component using the ADL, SARIMAX, and LSTM models, synthesize the component-level forecasts, and realize the comparison of electricity sales forecasting models based on different feature decomposition methods. The findings reveal (1) forecasting performance based on feature decomposition generally outperforms direct forecasting without decomposition; (2) different regions may benefit from different decomposition methods—EMD is more suitable for regions with high sales volatility, while DWT is preferable for more stable regions; and (3) among the forecasting models, ADL performs better than SARIMAX, while LSTM yields the least accurate results when combined with decomposition methods.

Keywords: electricity sales; feature decomposition; empirical mode decomposition (EMD); discrete wavelet transform (DWT); seasonal adjustment; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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