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Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Author

Listed:
  • Jun Dong

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Xihao Dou

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Aruhan Bao

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Yaoyu Zhang

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Dongran Liu

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Suggested Citation

  • Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7767-:d:847918
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    References listed on IDEAS

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    Cited by:

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    2. Jun Dong & Xihao Dou & Dongran Liu & Aruhan Bao & Dongxue Wang & Yunzhou Zhang & Peng Jiang, 2023. "Benefit Sharing of Power Transactions in Distributed Energy Systems with Multiple Participants," Sustainability, MDPI, vol. 15(11), pages 1-23, June.
    3. 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.

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