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Short-Term Power Load Forecasting Method Based on Improved Sparrow Search Algorithm, Variational Mode Decomposition, and Bidirectional Long Short-Term Memory Neural Network

Author

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  • Ming Wen

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
    Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China
    Hunan Key Laboratory of Energy Internet Supply-Demand and Operation, Changsha 410000, China)

  • Bo Liu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Hao Zhong

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Zongchao Yu

    (Economic and Technical Research Institute of State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China
    Hunan Key Laboratory of Energy Internet Supply-Demand and Operation, Changsha 410000, China)

  • Changqing Chen

    (Key Laboratory of Smart City Energy Sensing and Edge Computing of Hunan Province, Hunan City University, Yiyang 413000, China)

  • Xian Yang

    (Key Laboratory of Smart City Energy Sensing and Edge Computing of Hunan Province, Hunan City University, Yiyang 413000, China)

  • Xueying Dai

    (Key Laboratory of Smart City Energy Sensing and Edge Computing of Hunan Province, Hunan City University, Yiyang 413000, China)

  • Lisi Chen

    (Hunan Zhongdao New Energy Co., Ltd., Yiyang 413000, China)

Abstract

A short-term power load forecasting method is proposed based on an improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), and Bidirectional Long Short Term Memory (BiLSTM) neural network. First, the SSA is optimized by combining Tent chaotic mapping, reverse learning, and dynamic step adjustment strategy, and the VMD mode number and penalty factor are optimized by ISSA. Secondly, the initial load sequence is decomposed into several Intrinsic Mode Function (IMF) components using ISSA-VMD. The effective modal components are screened by Wasserstein Distance (WD) between IMF and the original signal probability density. Then, the effective modal components are reconstructed by the Improved Multi-scale Fast Sample Entropy (IMFSE) algorithm. Finally, the extracted features and IMF were input into the ISSA-BiLSTM model as input vectors for prediction.

Suggested Citation

  • Ming Wen & Bo Liu & Hao Zhong & Zongchao Yu & Changqing Chen & Xian Yang & Xueying Dai & Lisi Chen, 2024. "Short-Term Power Load Forecasting Method Based on Improved Sparrow Search Algorithm, Variational Mode Decomposition, and Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 17(21), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5280-:d:1505197
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    References listed on IDEAS

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    1. Morais, Lucas Barros Scianni & Aquila, Giancarlo & de Faria, Victor Augusto Durães & Lima, Luana Medeiros Marangon & Lima, José Wanderley Marangon & de Queiroz, Anderson Rodrigo, 2023. "Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system," Applied Energy, Elsevier, vol. 348(C).
    2. Tan, Mao & Liao, Chengchen & Chen, Jie & Cao, Yijia & Wang, Rui & Su, Yongxin, 2023. "A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor," Applied Energy, Elsevier, vol. 343(C).
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