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Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU

Author

Listed:
  • Zhuoqun Zou

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Jing Wang

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
    Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai 201306, China)

  • Ning E

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Can Zhang

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Zhaocai Wang

    (College of Information Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Enyu Jiang

    (Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R 2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting.

Suggested Citation

  • Zhuoqun Zou & Jing Wang & Ning E & Can Zhang & Zhaocai Wang & Enyu Jiang, 2023. "Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU," Energies, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6625-:d:1239959
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    References listed on IDEAS

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    1. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
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