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Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM

Author

Listed:
  • Fang Guo

    (School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China)

  • Shangyun Deng

    (School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China)

  • Weijia Zheng

    (School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China)

  • An Wen

    (Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Huzhou 313098, China)

  • Jinfeng Du

    (School of Information, Xuzhou Vocational College of Industrial Technology, Xuzhou 221140, China)

  • Guangshan Huang

    (School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China)

  • Ruiyang Wang

    (School of Mechatronical Engineering and Automation, Foshan University, Foshan 528231, China)

Abstract

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance.

Suggested Citation

  • Fang Guo & Shangyun Deng & Weijia Zheng & An Wen & Jinfeng Du & Guangshan Huang & Ruiyang Wang, 2022. "Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM," Energies, MDPI, vol. 15(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8445-:d:969965
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    References listed on IDEAS

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