Global solar radiation estimation using artificial neural network by the addition of nearby meteorological stations' solar radiation data and exergy of solar radiation: a case study

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dc.contributor.authorKurtgoz, Yusuf
dc.contributor.authorDeniz, Emrah
dc.date.accessioned2024-09-29T16:06:10Z
dc.date.available2024-09-29T16:06:10Z
dc.date.issued2016
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe artificial neural networks (ANNs) can be used to accurately predict the global solar radiation (GSR). There are many geographical, meteorological and terrestrial parameters affecting GSR. In this study, the most relevant of six input parameters are selected to predict the GSR of Goksun Station in Turkey using Waikato environment for knowledge analysis (Weka) Software. The effect of using nearby meteorological stations' GSR data as input on GSR prediction is investigated. Different ANN models are developed to demonstrate the difference between the exclusion and inclusion of these parameters on the model. The results show that the exclusion of less influential parameters and the inclusion of three nearby stations' GSR data has improved performance criteria. Petela, Spanner and Jeter's approaches are used for exergy analysis of measured and estimated GSR values. The mean exergy-to-energy ratio for both Petela and Spanner's approaches is 0.934, while Jeter's approach showed 0.950.en_US
dc.identifier.doi10.1504/IJEX.2016.079309
dc.identifier.endpage330en_US
dc.identifier.issn1742-8297
dc.identifier.issn1742-8300
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84989220451en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage315en_US
dc.identifier.urihttps://doi.org/10.1504/IJEX.2016.079309
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6680
dc.identifier.volume21en_US
dc.identifier.wosWOS:000393195600004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInderscience Enterprises Ltden_US
dc.relation.ispartofInternational Journal of Exergyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGSRen_US
dc.subjectglobal solar radiationen_US
dc.subjectsolar energyen_US
dc.subjectsolar exergyen_US
dc.subjectANNen_US
dc.subjectartificial neural networken_US
dc.subjectestimation of solar radiationen_US
dc.subjectWaikato environment for knowledge analysisen_US
dc.subjectWekaen_US
dc.titleGlobal solar radiation estimation using artificial neural network by the addition of nearby meteorological stations' solar radiation data and exergy of solar radiation: a case studyen_US
dc.typeArticleen_US

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