Several models to estimate daily global solar irradiation: adjustment and evaluation

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dc.contributor.authorSantos, Cicero Manoel dos
dc.contributor.authorTeramoto, Erico Tadao [UNESP]
dc.contributor.authorSouza, Amaury de
dc.contributor.authorAristone, Flavio
dc.contributor.authorIhaddadene, Razika
dc.contributor.institutionUniversidade Federal do Pará (UFPA)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de Mato Grosso do Sul (UFMS)
dc.contributor.institutionUniv MSila
dc.date.accessioned2021-06-25T12:38:20Z
dc.date.available2021-06-25T12:38:20Z
dc.date.issued2021-02-10
dc.description.abstractGlobal solar irradiation (Hg) is a variable of great importance for different applications. Despite the great advance in the latter, Hg measurements are not readily available in many places. So, several models with different input variables have been developed and used to estimate Hg worldwide. Estimating Hg from models that use air temperature offers an interesting alternative in the absence of measurements. Four groups of models were used in this study: empirical models group (M) with ten models, linear regression models group (RM) with six models, artificial neural network (ANN) models, and support vector machine (SVM) models groups with six models for each one. All the methods take into account different air temperature combinations as input variables (T-max, T-min, Delta T, and T-med). These models were applied to Campo Grande, Brazil, with 10 years data (2005-2017) in order to adjust and evaluate the Hg. The models were evaluated based on four statistical indexes: RMBE, RRMSE, MAPE, and WAI. The models were adjusted and validated using statistical techniques. The results show that the air temperature is an important entry point. Estimates with empirical models perform well (RMBE between 2.84 and 8.68%; RRMSE between 17.78 and 22.93%; MAPE between 17.23 and 23.31%; WAI between 0.85 and 0.90), with errors less than or equal to ANN models (RMBE between 13.94 and 22.67%; RRMSE between 25.37 and 34.95%; MAPE between 29.26 and 49.51%; WAI between 0.51 and 0.81) and SVM (RMBE between 5.80 and 10.15%; RRMSE between 18.87 and 30.34%; MAPE between 19.49 and 42.14%; WAI between 0.55 and 0.884). The regression models had errors with variations similar to the empirical and SVM models. The results show the need to adjust the local coefficients and further studies to further consolidate the models.en
dc.description.affiliationFed Univ, Fac Agron Engn UFPA, Rua Coronel Jose Porfirio 2515, BR-68372040 Altamira, PA, Brazil
dc.description.affiliationSao Paulo State Univ, Campus Expt Registro, Registro, SP, Brazil
dc.description.affiliationUniv Fed Mato Grosso do Sul, CP 549, BR-79070900 Campo Grande, MS, Brazil
dc.description.affiliationUniv MSila, Dept Mech Engn, Msila 28000, Algeria
dc.description.affiliationUnespSao Paulo State Univ, Campus Expt Registro, Registro, SP, Brazil
dc.format.extent16
dc.identifierhttp://dx.doi.org/10.1007/s12517-021-06603-8
dc.identifier.citationArabian Journal Of Geosciences. Heidelberg: Springer Heidelberg, v. 14, n. 4, 16 p., 2021.
dc.identifier.doi10.1007/s12517-021-06603-8
dc.identifier.issn1866-7511
dc.identifier.urihttp://hdl.handle.net/11449/210056
dc.identifier.wosWOS:000620090200003
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofArabian Journal Of Geosciences
dc.sourceWeb of Science
dc.subjectAir temperature
dc.subjectMachine learning
dc.subjectStatistical indices
dc.subjectBrazil
dc.titleSeveral models to estimate daily global solar irradiation: adjustment and evaluationen
dc.typeResenha
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
unesp.author.orcid0000-0003-3172-7520[4]
unesp.departmentEngenharia Agronômica - FCAVRpt

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Registro - FCAVR - Faculdade de Ciências Agrárias do Vale do Ribeira