Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions

International Journal of Energy ResearchVolume 46, Issue 8 p. 10052-10073 SPECIAL ISSUE RESEARCH ARTICLE Comparison of machine learning and deep learning algorithms for hourly global/diffuse solar radiation predictions Olusola Bamisile, Olusola Bamisile orcid.org/0000-0002-5154-6404 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorAriyo Oluwasanmi, Ariyo Oluwasanmi School of Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorChukwuebuka Ejiyi, Chukwuebuka Ejiyi School of Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorNasser Yimen, Nasser Yimen National Advanced School of Engineering, University of Yaounde I, Yaounde, CameroonSearch for more papers by this authorSandra Obiora, Sandra Obiora School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorQi Huang, Corresponding Author Qi Huang [email protected] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China Correspondence Qi Huang, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan China. Email: [email protected]Search for more papers by this author Olusola Bamisile, Olusola Bamisile orcid.org/0000-0002-5154-6404 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorAriyo Oluwasanmi, Ariyo Oluwasanmi School of Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorChukwuebuka Ejiyi, Chukwuebuka Ejiyi School of Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorNasser Yimen, Nasser Yimen National Advanced School of Engineering, University of Yaounde I, Yaounde, CameroonSearch for more papers by this authorSandra Obiora, Sandra Obiora School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorQi Huang, Corresponding Author Qi Huang [email protected] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China Correspondence Qi Huang, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan China. Email: [email protected]Search for more papers by this author First published: 16 February 2021 https://doi.org/10.1002/er.6529Citations: 10 Funding information: Sichuan Youth Science and Technology Innovation Team Fund, Grant/Award Number: 2017TD0009 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 Summary Due to the advancement and wide adoption/application of solar-based technologies, the prediction of solar irradiance has attracted research attention in recent years. In this study, the predictive performance of machine learning models is compared with that of deep learning models for both global solar radiation (GSR) and diffuse solar radiation (DSR) prediction. Different studies have proposed the use of different models for solar radiation prediction. While some used machine learning models, the use of deep learning algorithms were considered by others. Although these algorithms were concluded to be appropriate for solar radiation prediction, variation in their performances brings about an intriguing quest to compare and determine the most appropriate algorithm. The three most common deep learning models in the literature namely; artificial neural network, convolutional neural network, and recurrent neural network (RNN) are considered within the scope of this study. Also, two traditional machine learning models namely polynomial regression and support vector regression (SVR) is considered as well as an ensemble machine learning model called random forest. These models have been applied to four different locations in Nigeria and the typical meteorological year data for 12 years in an hourly time step was used to train/test the model developed. Results from this study show that deep learning models have a better GSR and DSR prediction accuracy in comparison to machine learning models. However, the duration for training and testing the machine learning models (except SVR) is shorter than that of deep learning models making it more desirable for low computational applications. The application of RNN for GSR prediction in Yobe (with an r value of 0.9546 and root means square error/mean absolute error of 82.22 W/m2/36.52 W/m2) had the overall best model performance of all the models developed in this study. This study contributes to the existing literature in this field as it highlights the disparities between machine learning and deep learning algorithms application for solar radiation forecast. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available on request from the first/corresponding author upon reasonable request. Citing Literature Volume46, Issue8Special Issue: Potential Energy Solutions (IEEES‐12)25 June 2022Pages 10052-10073 RelatedInformation