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Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting

Author

Listed:
  • Wenting Zhao

    (School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China
    Shanxi Key Laboratory of Data Element Innovation and Economic Decision Analysis, Taiyuan 030024, China)

  • Haoran Xu

    (School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Peng Chen

    (School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China)

  • Juan Zhang

    (School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China)

  • Jing Li

    (School of Economic and Management, Taiyuan University of Technology, Jinzhong 030600, China)

  • Tingting Cai

    (School of Safety and Emergency Management Engineering, Taiyuan University of Technology, Jinzhong 030600, China)

Abstract

Load forecasting plays a crucial role in power system planning and operational dispatch management. Accurate load prediction is essential for enhancing power system reliability and facilitating the local integration of renewable energy. This paper proposes a hybrid approach combining traditional time series models (ARIMA) with machine learning models (SVR). The particle swarm optimization (PSO) algorithm is improved by adjusting its elastic momentum, and the enhanced APSO algorithm is employed to optimize the adaptive weights of the hybrid model. Consequently, an elastic momentum-enhanced adaptive weighted load forecasting model (APSO-ARIMA-SVR) is developed. Numerical simulations using real-world datasets validate the model’s effectiveness. Results demonstrate that the proposed APSO-ARIMA-SVR model achieves optimal fitting performance, with prediction errors of 274.23 (MAE) and 321.50 (RMSE), representing the lowest errors among all comparative models.

Suggested Citation

  • Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3263-:d:1684677
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