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Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Author

Listed:
  • Laiqing Yan

    (School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, China
    These authors contributed equally to this work.)

  • Zutai Yan

    (School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, China
    These authors contributed equally to this work.)

  • Zhenwen Li

    (School of Electric Power, Civil Engineering and Architecture, Shanxi University, Taiyuan 030031, China)

  • Ning Ma

    (North China Electric Power Research Institute Co., Ltd., Beijing 100045, China)

  • Ran Li

    (State Grid Taiyuan Electric Power Supply Company, Taiyuan 030000, China)

  • Jian Qin

    (State Grid Taiyuan Electric Power Supply Company, Taiyuan 030000, China)

Abstract

Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.

Suggested Citation

  • Laiqing Yan & Zutai Yan & Zhenwen Li & Ning Ma & Ran Li & Jian Qin, 2023. "Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm," Energies, MDPI, vol. 16(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5098-:d:1184881
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    References listed on IDEAS

    as
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    1. Vasileios Laitsos & Georgios Vontzos & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2024. "Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms," Energies, MDPI, vol. 17(7), pages 1-27, March.

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