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SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition

Author

Listed:
  • Chengfei Qi

    (Metrology Center of State Grid Jibei Electric Power Co., Ltd., Beijing 100045, China)

  • Yanli Feng

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China)

  • Junling Wan

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China
    Labs of Advanced Data Science and Service, Nanjing Agricultural University, Nanjing 211800, China)

  • Xinying Mao

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China
    Labs of Advanced Data Science and Service, Nanjing Agricultural University, Nanjing 211800, China)

  • Peisen Yuan

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China
    Labs of Advanced Data Science and Service, Nanjing Agricultural University, Nanjing 211800, China)

Abstract

Short-term load forecasting is important for proxy electricity purchasing in the electricity spot trading market. In this paper, a model SFPFMformer for short-term power load forecasting is proposed to address the issue of balancing accuracy and timeliness. In SFPFMformer, the random forest algorithm is applied to select the most important attributes, which reduces redundant attributes and improves performance and efficiency; then, multiple timescale segmentation is used to extract load data features from multiple time dimensions to learn feature representations at different levels. In addition, fusion time location encoding is adopted in Transformer to ensure that the model can accurately capture time-position information. Finally, we utilize a depthwise separable convolution block to extract features from power load data, which efficiently captures the pattern of change in load. We conducted extensive experiment on real datasets, and the experimental results show that in 4 h prediction, the RMSE, MAE, and MAPE of our model are 1128.69, 803.91, and 2.63%, respectively. For 24 h forecast, the RMSE, MAE and MAPE of our model are 1190.51, 897.26, and 2.97%, respectively. Compared with existing methods, such as Informer, Autoformer, ETSformer, LSTM, and Seq2seq, our model has better precision and time performance for short-term power load forecasting for proxy spot trading.

Suggested Citation

  • Chengfei Qi & Yanli Feng & Junling Wan & Xinying Mao & Peisen Yuan, 2025. "SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition," Mathematics, MDPI, vol. 13(10), pages 1-22, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1584-:d:1653949
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    References listed on IDEAS

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