A <scp>CNN‐BiLSTM</scp> based deep learning model for mid‐term solar radiation prediction

International Transactions on Electrical Energy SystemsVolume 31, Issue 9 e12664 SPECIAL ISSUE PAPER A CNN-BiLSTM based deep learning model for mid-term solar radiation prediction Amit Rai, Amit Rai Electrical Engineering Department, Indian Institute of Technology(ISM), Dhanbad, IndiaSearch for more papers by this authorAshish Shrivastava, Corresponding Author Ashish Shrivastava rewa.ashish@gmail.com orcid.org/0000-0002-2465-0708 Electrical Engineering Department, Manipal University Jaipur, Jaipur, India Correspondence Ashish Shrivastava, Electrical Engineering Department, Manipal University Jaipur, Jaipur, India Email: rewa.ashish@gmail.comSearch for more papers by this authorKartick C Jana, Kartick C Jana Electrical Engineering Department, Indian Institute of Technology(ISM), Dhanbad, IndiaSearch for more papers by this author Amit Rai, Amit Rai Electrical Engineering Department, Indian Institute of Technology(ISM), Dhanbad, IndiaSearch for more papers by this authorAshish Shrivastava, Corresponding Author Ashish Shrivastava rewa.ashish@gmail.com orcid.org/0000-0002-2465-0708 Electrical Engineering Department, Manipal University Jaipur, Jaipur, India Correspondence Ashish Shrivastava, Electrical Engineering Department, Manipal University Jaipur, Jaipur, India Email: rewa.ashish@gmail.comSearch for more papers by this authorKartick C Jana, Kartick C Jana Electrical Engineering Department, Indian Institute of Technology(ISM), Dhanbad, IndiaSearch for more papers by this author First published: 17 October 2020 https://doi.org/10.1002/2050-7038.12664Citations: 1Read 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 onFacebookTwitterLinkedInRedditWechat Abstract The penetrations of solar power plants are increasing their presence worldwide. The solar power plants have uncertain power output as its output depends on solar radiation, which is environmental dependent, so solar radiation prediction is a crucial step in integrating these plants into the power grid. In this work, a convolution neural network (CNN) and bi-direction long short term memory (BiLSTM) based hybrid deep learning (DL) model is proposed for effective midterm solar radiation prediction. The CNN architecture in this model captures the feature in solar radiation input data, and BiLSTM exploits the dependencies of this time series data. The proposed model is tested for three different geographical locations on the same latitude as it receives approximately the same solar radiation. The proposed hybrid DL model is compared with different recently proposed DL models. Moreover, distribution errors such as, skew and kurtosis errors are included for evaluating the distribution of predicted solar radiation. The outcome shows that the proposed hybrid DL model is robust and further enhancing the recently proposed DL models for midterm solar radiation prediction. Open Research PEER REVIEW The peer review history for this article is available at https://publons.com/publon/10.1002/2050-7038.12664. DATA AVAILABILITY The data that support the findings of this study are openly available in https://re.jrc.ec.europa.eu/pvg_tools/en/#PVP.31 Citing Literature Volume31, Issue9September 2021e12664 RelatedInformation