Enhanced Short-Term Photovoltaic Power Prediction Through Multi-Method Data Processing and SFOA-Optimized CNN-BiLSTM

Xiaojun Hua, Zhiming Zhang, Tao Ye, Zida Song, Yun Shao and Yixin Su ()
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Xiaojun Hua: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430010, China
Zhiming Zhang: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Tao Ye: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430010, China
Zida Song: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430010, China
Yun Shao: Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430010, China
Yixin Su: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Energies, 2025, vol. 18, issue 19, 1-20

Abstract: The increasing global demand for renewable energy poses significant challenges to grid stability due to the fluctuation and unpredictability of photovoltaic (PV) power generation. To enhance the accuracy of short-term PV power prediction, this study proposes an innovative integrated model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), optimized using the Starfish Optimization Algorithm (SFOA) and integrated with a multi-method data processing framework. To reduce input feature redundancy and improve prediction accuracy under different conditions, the K-means clustering algorithm is employed to classify past data into three typical weather scenarios. Empirical Mode Decomposition is utilized for multi-scale feature extraction, while Kernel Principal Component Analysis is applied to reduce data redundancy by extracting nonlinear principal components. A hybrid CNN-BiLSTM neural network is then constructed, with its hyperparameters optimized using SFOA to enhance feature extraction and sequence modeling capabilities. The experiments were carried out with historical data from a Chinese PV power station, and the results were compared with other existing prediction models. The results demonstrate that the Root Mean Square Error of PV power generation prediction for three scenarios are 9.8212, 12.4448, and 6.2017, respectively, outperforming all other comparative models.

Keywords: short-term PV power prediction; empirical mode decomposition; kernel principal component analysis; neural network; starfish optimization algorithm (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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