PHOTOVOLTAIC POWER COMBINATION PREDICTION MODEL BASED ON CEEMDAN-SE AND SERIAL CNN-GRU

Dou Zhenlan; Wu Songmei; Guo Hui; Zhang Chunyan; Wang Fei

doi:10.19912/j.0254-0096.tynxb.2024-1760

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 67-75. DOI: 10.19912/j.0254-0096.tynxb.2024-1760

PHOTOVOLTAIC POWER COMBINATION PREDICTION MODEL BASED ON CEEMDAN-SE AND SERIAL CNN-GRU

Dou Zhenlan¹, Wu Songmei², Guo Hui², Zhang Chunyan¹, Wang Fei²

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Abstract

To enhance the accuracy of PV power forecasting, this paper proposes a combined forecasting model for PV power based on CEEMDAN and a serial CNN-GRU network. Firstly, considering the impact of PV power fluctuations on the forecasting results, CEEMDAN is used to decompose the original PV power into several subsequences to reduce the non-stationarity of the sequence. Each subsequence’s sample entropy（SE） is then calculated to measure its complexity, and subsequences with similar SE values are regrouped to reduce computational load. Secondly, to overcome the limitations of a single neural network in learning the historical characteristics of PV power, a serial CNN-GRU hybrid neural network is proposed to explore the spatiotemporal features of PV powerfully. Each subsequence is input into the serial CNN-GRU network to obtain the forecasting results, and the predicted values of the subsequences are summed to yield the final PV power forecasting results. Finally, case studies on PV power plants in two regions are conducted, with comparative validation against LSTM, GRU, CEEMDAN-LSTM, CEEMDAN-GRU, and the serial CNN-GRU models. The results show that the proposed model achieves superior forecasting accuracy and generalization capability.

Key words

photovoltaic power forecasting / CNN / GRU / hybrid neural networks / CEEMDAN / SE

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Dou Zhenlan, Wu Songmei, Guo Hui, Zhang Chunyan, Wang Fei. PHOTOVOLTAIC POWER COMBINATION PREDICTION MODEL BASED ON CEEMDAN-SE AND SERIAL CNN-GRU[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 67-75 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1760

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