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

Shengli Wang, Xiaolong Guo, Tianle Sun, Lihui Xu, Jinfeng Zhu, Zhicai Li and Jinjiang Zhang ()
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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

Energies, 2025, vol. 18, issue 2, 1-17

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.

Keywords: photovoltaic power forecast; K-means; improved dung beetle optimizer; variational mode decomposition; deep hybrid learning; kernel extremum learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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