Comparison of the advanced machine learning methods for better prediction accuracy of solar radiation using only temperature data: A case study

International Journal of Energy ResearchVolume 46, Issue 3 p. 2709-2736 RESEARCH ARTICLE Comparison of the advanced machine learning methods for better prediction accuracy of solar radiation using only temperature data: A case study Amin Mirbolouki, Amin Mirbolouki Faculty of Engineering, Kharazmi University, Tehran, IranSearch for more papers by this authorSalim Heddam, Salim Heddam orcid.org/0000-0002-8055-8463 Faculty of Science, Agronomy Department, Hydraulics Division University, Skikda, AlgeriaSearch for more papers by this authorKulwinder Singh Parmar, Kulwinder Singh Parmar Department of Mathematics, IKG Punjab Technical University, Kapurthala, IndiaSearch for more papers by this authorSlavisa Trajkovic, Slavisa Trajkovic Faculty of Civil Engineering & Architecture, University of Nis, Nish, SerbiaSearch for more papers by this authorMojtaba Mehraein, Corresponding Author Mojtaba Mehraein mehraein@khu.ac.ir orcid.org/0000-0002-2819-253X Faculty of Engineering, Kharazmi University, Tehran, Iran Correspondence Mojtaba Mehraein, Faculty of Engineering, Kharazmi University, Tehran, Iran. Email: mehraein@khu.ac.irSearch for more papers by this authorOzgur Kisi, Ozgur Kisi School of Technology, Ilia State University, Tbilisi, GeorgiaSearch for more papers by this author Amin Mirbolouki, Amin Mirbolouki Faculty of Engineering, Kharazmi University, Tehran, IranSearch for more papers by this authorSalim Heddam, Salim Heddam orcid.org/0000-0002-8055-8463 Faculty of Science, Agronomy Department, Hydraulics Division University, Skikda, AlgeriaSearch for more papers by this authorKulwinder Singh Parmar, Kulwinder Singh Parmar Department of Mathematics, IKG Punjab Technical University, Kapurthala, IndiaSearch for more papers by this authorSlavisa Trajkovic, Slavisa Trajkovic Faculty of Civil Engineering & Architecture, University of Nis, Nish, SerbiaSearch for more papers by this authorMojtaba Mehraein, Corresponding Author Mojtaba Mehraein mehraein@khu.ac.ir orcid.org/0000-0002-2819-253X Faculty of Engineering, Kharazmi University, Tehran, Iran Correspondence Mojtaba Mehraein, Faculty of Engineering, Kharazmi University, Tehran, Iran. Email: mehraein@khu.ac.irSearch for more papers by this authorOzgur Kisi, Ozgur Kisi School of Technology, Ilia State University, Tbilisi, GeorgiaSearch for more papers by this author First published: 02 October 2021 https://doi.org/10.1002/er.7341Citations: 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 onFacebookTwitterLinked InRedditWechat Summary Estimation of solar radiation (SR) carries importance for planning available renewable energy, and it is also beneficial for solving agricultural, meteorological, and engineering problems. This study compares the ability of hybrid adaptive neuro fuzzy (ANFIS) models and long short-term memory to search a suitable approach for SR prediction with minimum number of input parameters (temperature) in Mediterranean region of Turkey, which could be useful for the regions in which other effective parameters (eg, relative humidity, wind speed) are not available. The models considered were assessed by considering four data splitting scenarios, 50% train—50% test, 60% train—40% test, 70% train—30% test, and 80% train—20% test. Among the hybrid methods, the ANFIS with grey wolf optimization and genetic algorithm showed a superior accuracy. The study shows that applying different data splitting scenarios is necessary for better assessment of the data-driven methods since the accuracies of the implemented methods increase by about 30% to 60% when the splitting data scenario varies from 50-50% to 80-20%. Sensitivity analysis shows that the performance of the model increases by about 40% using extraterrestrial radiation for the best model. The ANFIS with grey wolf optimization and genetic algorithm is recommended to predict monthly solar radiation with limited input data. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. Citing Literature Volume46, Issue310 March 2022Pages 2709-2736 RelatedInformation