Photovoltaic Power Prediction Based on Similar Day Clustering Combined with CNN-GRU

Chao Gao, Shuai Zhang, Zhiqin Li, Bin Zhou, Dong Guo (), Wenqi Shao and Haowen Li
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Chao Gao: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Shuai Zhang: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Zhiqin Li: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Bin Zhou: State Key Laboratory of Intelligent Transportation System, Beijing 210096, China
Dong Guo: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Wenqi Shao: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
Haowen Li: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China

Sustainability, 2025, vol. 17, issue 16, 1-20

Abstract: In order to address the challenge of achieving optimal prediction accuracy when a single prediction model faced with changes in meteorological conditions of different weather types, this paper proposes a photovoltaic (PV) power prediction method based on the combination of similar day clustering and convolutional neural network (CNN)-gated recurrent unit (GRU). The Pearson correlation coefficient and Spearman’s correlation coefficient are used to filter out the key features such as total solar radiation and module temperature to construct a new input dataset; the K-means algorithm is used to perform clustering analysis on the data, and the data are classified into sunny, cloudy, and rainy days; the spatial correlation features of the meteorological factors are extracted by using the convolutional neural network (CNN), and the CNN-GRU model is established by combining with the gated recurrent units (GRUs). The PV output power is predicted based on the PV power data and the corresponding meteorological data from a place in Ningxia, collected during June to August 2020, and the method proposed in the article is tested. Validation results show that, compared to other models, the model proposed in this paper reduces MAE and RMSE by 66.1% and 65.7% on average under three different weather type scenarios, and improves R 2 by 19.8% on average. This verifies that the model has high prediction accuracy and generalization ability, achieving better results in PV output power prediction. The CNN-GRU model demonstrates superior capability in modeling short- and long-term dependencies compared to other deep learning hybrid approaches, while also achieving higher computational efficiency and faster training convergence.

Keywords: photovoltaic power generation; power prediction; K-means; convolutional neural network; Gated circulation units (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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