Ultra‐short‐term photovoltaic power forecasting of multifeature based on hybrid deep learning

International Journal of Energy ResearchVolume 46, Issue 2 p. 1370-1386 RESEARCH ARTICLE Ultra-short-term photovoltaic power forecasting of multifeature based on hybrid deep learning Yanguo Huang, Yanguo Huang School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSearch for more papers by this authorManguo Zhou, Corresponding Author Manguo Zhou 823185089@qq.com School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China Correspondence Manguo Zhou, School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000 China. Email: 823185089@qq.comSearch for more papers by this authorXungen Yang, Xungen Yang School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSearch for more papers by this author Yanguo Huang, Yanguo Huang School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSearch for more papers by this authorManguo Zhou, Corresponding Author Manguo Zhou 823185089@qq.com School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China Correspondence Manguo Zhou, School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000 China. Email: 823185089@qq.comSearch for more papers by this authorXungen Yang, Xungen Yang School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, ChinaSearch for more papers by this author First published: 06 September 2021 https://doi.org/10.1002/er.7254 Funding information: Education Department of Jiangxi Province, Grant/Award Number: GJJ160609; National Natural Science Foundation of China, Grant/Award Number: 72061016; Jiangxi University of Science and Technology Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary The output power of photovoltaic power has randomness and volatility, which poses new challenges to the peak shaving and dispatching of the power system. Therefore, the accurate prediction of photovoltaic power output is an effective way to maintain the security and stability of the power grid. In this article, a hybrid prediction model based on improved convolutional neural network and bidirectional gated recurrent unit is proposed to predict PV output power. Firstly, the concept of time relevance is introduced to fuse this feature with weather characteristics as the input of the prediction model. Secondly, the time series generated adversarial neural network is applied to the field of photovoltaic power generation prediction for the first time to enhance the dataset obtained. Thirdly, the model proposed in this article is trained and compared with other models under different characteristics, different weather conditions, and different forecast time steps. The experimental results show that the hybrid deep learning model proposed in this article has better prediction performance. Under different characteristics, the model proposed in this article has the highest prediction accuracy. After data enhancement, the prediction accuracy of each model has been improved by 1% to 3%. Among them, the prediction accuracy of the model proposed in this article is 0.977 and 0.960, respectively. Under different weather conditions and different time steps, the proposed model has the best stability. Under cloudy and rainy weather, the prediction accuracy of the proposed model only decreases by about 6%. In addition, the prediction accuracy of the proposed model only decreases by approximately 1.5% to 4% from 15 to 180 minutes. Volume46, Issue2February 2022Pages 1370-1386 RelatedInformation