An Informer Model for Very Short-Term Power Load Forecasting

Zhihe Yang, Jiandun Li (), Haitao Wang and Chang Liu
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Zhihe Yang: School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China
Jiandun Li: School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China
Haitao Wang: School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China
Chang Liu: School of Electronic and Information Engineering, Shanghai Dianji University, Shanghai 201306, China

Energies, 2025, vol. 18, issue 5, 1-16

Abstract: Facing the decarbonization trend in power systems, there appears to be a growing requirement on agile response and delicate supply from electricity suppliers. To accommodate this request, it is of key significance to precisely extrapolate the upcoming power load, which is well acknowledged as VSTLF, i.e., Very Short-Term Power Load Forecasting. As a time series forecasting problem, the primary challenge of VSTLF is how to identify potential factors and their very long-term affecting mechanisms in load demands. With the help of a public dataset, this paper first locates several intensely related attributes based on Pearson’s correlation coefficient and then proposes an adaptive Informer network with the probability sparse attention to model the long-sequence power loads. Additionally, it uses the Shapley Additive Explanations (SHAP) for ablation and interpretation analysis. The experiment results show that the proposed model outperforms several state-of-the-art solutions on several metrics, e.g., 18.39% on RMSE, 21.70% on MAE, 21.24% on MAPE, and 2.11% on R 2 .

Keywords: very short-term power load forecasting; very long sequence time series forecasting; transformer; informer; Shapley additive explanations (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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