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

Chengfei Qi, Yanli Feng, Junling Wan, Xinying Mao and Peisen Yuan ()
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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
Xinying Mao: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China
Peisen Yuan: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 211800, China

Mathematics, 2025, vol. 13, issue 10, 1-22

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.

Keywords: depthwise separable convolution; fusion time localization encoding; multiple timescale; proxy electricity purchase; short-term power load forecasting; Transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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