Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

• Published in: Energies (Basel) Vol. 17; no. 13; p. 3073
• Main Authors: Cheng, Hsu-Yung, Yu, Chih-Chang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Alternative energy sources; Datasets; Deep learning; Energy management; gated recurrent unit; Machine learning; Neural networks; photovoltaics; power generation prediction; Renewable resources; Seasonal variations; Solar energy; Statistical methods; Time series; Taiwan
• DOI: 10.3390/en17133073
• ISSN: 1996-1073

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.