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Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management

Author

Listed:
  • Fan Yu

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Lei Wang

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
    Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China)

  • Qiaoyong Jiang

    (Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an 710048, China)

  • Qunmin Yan

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

  • Shi Qiao

    (College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China)

Abstract

Accurate and rapid forecasting of short-term loads facilitates demand-side management by electricity retailers. The complexity of customer demand makes traditional forecasting methods incapable of meeting the accuracy requirements, so a self-attention based short-term load forecasting (STLF) considering demand-side management is proposed. In the data preprocessing stage, non-parametric kernel density estimation is used to construct customer electricity consumption feature curves, and then historical load data are used to delineate the feasible domain range for outlier detection. In the feature selection stage, the feature data are selected using variational modal decomposition and a maximum information coefficient to enhance the model prediction accuracy. In the model prediction stage, the decomposed intrinsic mode function components are independently predicted and reconstructed using an Informer based on improved self-attention. Additionally, the novel AdaBlief optimizer is used to optimize the model parameters. Cross-sectional and longitudinal experiments are conducted on a regional-level load dataset set in Spain. The experimental results prove that the proposed method is superior to other methods in STLF.

Suggested Citation

  • Fan Yu & Lei Wang & Qiaoyong Jiang & Qunmin Yan & Shi Qiao, 2022. "Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management," Energies, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4198-:d:833496
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    References listed on IDEAS

    as
    1. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    2. Andrés M. Alonso & Francisco J. Nogales & Carlos Ruiz, 2020. "A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series," Energies, MDPI, vol. 13(20), pages 1-19, October.
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    Cited by:

    1. Fatma Yaprakdal, 2022. "An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods," Energies, MDPI, vol. 16(1), pages 1-13, December.

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