Application of machine learning for solar radiation modeling

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dc.authoridRohani, Abbas/0000-0002-4494-7058
dc.authoridTaki, Morteza/0000-0002-3059-4984
dc.contributor.authorTaki, Morteza
dc.contributor.authorRohani, Abbas
dc.contributor.authorYildizhan, Hasan
dc.date.accessioned2025-01-06T17:38:11Z
dc.date.available2025-01-06T17:38:11Z
dc.date.issued2021
dc.description.abstractSolar radiation is an important parameter that affects the atmosphere-earth thermal balance and many water and soil processes such as evapotranspiration and plant growth. The modeling of the daily and monthly solar radiation by Gaussian process regression (GPR) with K-fold cross-validation model has been discussed recently. This study evaluated different neural models such as artificial neural network (ANN), support vector machine (SVM), adaptive network-based fuzzy inference system (ANFIS), and multiple linear regression (MLR) for estimating the global solar radiation (daily and monthly) with K-fold cross-validation method. For the appropriate comparison of the models, the randomized complete block (RCB) design applied in the training and test phases. Also, different data sets were evaluated by K-fold cross-validation in each model. The results showed that radial basis function (RBF) model has the lowest error for estimating the monthly and daily solar radiation. In this study, the result of RBF was compared with the GPR models. The conclusion indicated that RBF methodology can predict solar radiation with higher accuracy relative to the GPR model. The results of yearly solar radiation estimation (2009-2014) showed that the RBF model can estimate solar radiation with the MAPE and RMSE of 5.1% and 0.29, respectively. Also, the coefficient of correlation (R-2) between actual and estimated values throughout the year is 98% and can be used by the engineers and other researchers for solar and thermal applications.
dc.description.sponsorshipAgricultural Sciences and Natural Resources University of Khuzestan, Iran
dc.description.sponsorshipThe authors would like to thank the editor in chief and the anonymous referees for their valuable suggestions and useful comments that improved the paper content substantially. Also special thanks to the Meteorological Office Data Center in Khorasan Razavi, Iran, for providing data related to this study. This study was supported by Agricultural Sciences and Natural Resources University of Khuzestan, Iran. The authors are grateful for the support provided by this university.
dc.identifier.doi10.1007/s00704-020-03484-x
dc.identifier.endpage1613
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue3-4
dc.identifier.scopus2-s2.0-85099216706
dc.identifier.scopusqualityQ2
dc.identifier.startpage1599
dc.identifier.urihttps://doi.org/10.1007/s00704-020-03484-x
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2511
dc.identifier.volume143
dc.identifier.wosWOS:000606234200002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Wien
dc.relation.ispartofTheoretical and Applied Climatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGlobal solar radiation
dc.subjectModeling
dc.subjectSupport vector machine
dc.subjectRadial bias function
dc.titleApplication of machine learning for solar radiation modeling
dc.typeArticle

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