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A hybrid algorithm for multi-step prediction of ultra-short-term electricity load considering an innovative preprocessing method

Author

Listed:
  • Qiao, Weibiao
  • Shi, Luyao
  • Kong, Xiangzhe
  • Yang, Xinjun
  • Huang, Nan
  • Wang, Yuqin

Abstract

Ultra-short-term electricity load (USTEL) is an important factor for the safety and stability of the electricity system. However, due to the strong nonlinearity and fluctuations of USTEL, accurately predicting USTEL has become very difficult, especially for multi-step forecasts. To address this issue, a novel multi-step prediction model is formed. Firstly, intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) is adopted to decompose the USTEL into several components, and approximate entropy (AE) is applied to analyze the complexity of the different components. Secondly, in view of the advantages of stacked autoencoder (SAE) and long short-term memory (LSTM), a coupled forecast model is established. Thirdly, ICEEMDAN, AE, SAE, and LSTM are combined. Finally, taking USTEL in New South Wales, Australia as an example, over 3.15 × 105 raw data points are collected. The results show that: (1) for USTEL in different seasons, the established prediction model has higher forecasting accuracy and stability than the other two models at different forecasting steps, based on different error evaluation indicators; (2) compared with some advanced prediction models, the proposed prediction model is more excellent. Through the prediction results, we can conclude that the prediction model formed is excellent based on forecasting accuracy and stability.

Suggested Citation

  • Qiao, Weibiao & Shi, Luyao & Kong, Xiangzhe & Yang, Xinjun & Huang, Nan & Wang, Yuqin, 2026. "A hybrid algorithm for multi-step prediction of ultra-short-term electricity load considering an innovative preprocessing method," Renewable Energy, Elsevier, vol. 256(PG).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pg:s0960148125020804
    DOI: 10.1016/j.renene.2025.124416
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