Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

Hsu-Yung Cheng () and Chih-Chang Yu
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Hsu-Yung Cheng: Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320314, Taiwan
Chih-Chang Yu: Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan

Energies, 2024, vol. 17, issue 13, 1-18

Abstract: For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan.

Keywords: photovoltaics; solar energy; power generation prediction; gated recurrent unit (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: 2024
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