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Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model

Author

Listed:
  • Shengli Wang

    (State Grid Kashgar Power Supply Company, Kashgar 844000, China)

  • Xiaolong Guo

    (State Grid Kashgar Power Supply Company, Kashgar 844000, China)

  • Tianle Sun

    (School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Lihui Xu

    (State Grid Kashgar Power Supply Company, Kashgar 844000, China)

  • Jinfeng Zhu

    (State Grid Kashgar Power Supply Company, Kashgar 844000, China)

  • Zhicai Li

    (State Grid Kashgar Power Supply Company, Kashgar 844000, China)

  • Jinjiang Zhang

    (School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)

Abstract

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model’s hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy.

Suggested Citation

  • Shengli Wang & Xiaolong Guo & Tianle Sun & Lihui Xu & Jinfeng Zhu & Zhicai Li & Jinjiang Zhang, 2025. "Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model," Energies, MDPI, vol. 18(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:403-:d:1569696
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    References listed on IDEAS

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    5. Zhang, Mingyue & Han, Yang & Wang, Chaoyang & Yang, Ping & Wang, Congling & Zalhaf, Amr S., 2024. "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, Elsevier, vol. 375(C).
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