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Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Author

Listed:
  • Jiajie Tang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jie Zhao

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Hongliang Zou

    (Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Taizhou 318000, China)

  • Gaoyuan Ma

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jun Wu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xu Jiang

    (Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Taizhou 318000, China)

  • Huaixun Zhang

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article. Firstly, the bus load series was decomposed into a group of relatively stable subsequence components by VMD to reduce the interaction between different trend information. Then, a time series prediction model based on Bi-LSTM was constructed for each sub sequence, and Bayesian theory was used to optimize the sub sequence-related hyperparameters and judge whether the sequence uses Bi-LSTM to improve the prediction accuracy of a single model. Finally, the bus load prediction value was obtained by superimposing the prediction results of each subsequence. The example results show that compared with the traditional prediction algorithm, the proposed method can better track the change trend of bus load, and has higher prediction accuracy and stability.

Suggested Citation

  • Jiajie Tang & Jie Zhao & Hongliang Zou & Gaoyuan Ma & Jun Wu & Xu Jiang & Huaixun Zhang, 2021. "Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10526-:d:640906
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    References listed on IDEAS

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    5. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
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    Cited by:

    1. Yixiang Ma & Lean Yu & Guoxing Zhang, 2022. "A Hybrid Short-Term Load Forecasting Model Based on a Multi-Trait-Driven Methodology and Secondary Decomposition," Energies, MDPI, vol. 15(16), pages 1-20, August.

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