Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions

Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang and Siwei Zhang ()
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Yuxiang Guo: Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Taipa, Macao 999078, China
Qiang Han: Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Taipa, Macao 999078, China
Tan Li: IKAS Industries Co., Ltd., Beijing 100000, China
Huichu Fu: IKAS Industries Co., Ltd., Beijing 100000, China
Meng Liang: IKAS Industries Co., Ltd., Beijing 100000, China
Siwei Zhang: Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Taipa, Macao 999078, China

Mathematics, 2025, vol. 13, issue 11, 1-34

Abstract: The rapid expansion of global photovoltaic (PV) capacity has imposed higher demands on forecast accuracy and timeliness in power dispatching. However, traditional PV power forecasting models designed for distributed PV power stations often struggle with accuracy due to unpredictable meteorological variations, data noise, non-stationary signals, and human-induced data collection errors. To effectively mitigate these limitations, this work proposes a dual-stage feature extraction method based on Variational Mode Decomposition (VMD) and Principal Component Analysis (PCA), enhancing multi-scale modeling and noise reduction capabilities. Additionally, the Whale Optimization Algorithm is adopted to efficiently optimize the hyperparameters of iTransformer for the framework, improving parameter adaptability and convergence efficiency. Based on VMD-PCA refined feature extraction, the iTransformer is then employed to perform continuous active power prediction across time steps, leveraging its strength in modeling long-range temporal dependencies under complex meteorological conditions. Experimental results demonstrate that the proposed model exhibits superior robustness across multiple evaluation metrics, including coefficient of determination, mean square error, mean absolute error, and root mean square error, with comparatively low latency. This research provides valuable model support for reliable PV system dispatch and its application in smart grids.

Keywords: PV power forecasting; variational mode decomposition; principal component analysis; whale optimization algorithm; iTransformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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