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An Informer Model for Very Short-Term Power Load Forecasting

Author

Listed:
  • 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)

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 .

Suggested Citation

  • Zhihe Yang & Jiandun Li & Haitao Wang & Chang Liu, 2025. "An Informer Model for Very Short-Term Power Load Forecasting," Energies, MDPI, vol. 18(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1150-:d:1600240
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    References listed on IDEAS

    as
    1. Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.
    2. Hu, Yi & Qu, Boyang & Wang, Jie & Liang, Jing & Wang, Yanli & Yu, Kunjie & Li, Yaxin & Qiao, Kangjia, 2021. "Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning," Applied Energy, Elsevier, vol. 285(C).
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