Solar-energy potential in Turkey

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dc.contributor.authorSözen, Adnan
dc.contributor.authorArcaklıoğlu, Erol
dc.contributor.authorÖzalp, Mehmet
dc.contributor.authorKanıt, E. Galip
dc.date.accessioned2020-06-25T17:40:41Z
dc.date.available2020-06-25T17:40:41Z
dc.date.issued2005
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractIn this study, a new formula based on meteorological and geographical data was developed to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. Meteorological data for the last four years (2000 2003) from 18 cities (Bilecik, Kirsehir, Akhisar, Bingol, Batman, Bodrum, Uzunkopru", Sile, Bartin, Yalova, Horasan, Polath, Malazgirt, Koycegiz, Manavgat, Dortyol, Karatas and Birecik) spread over Turkey were used as data in order to train the neural network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) were used in the input layer of the network. Solar radiation is the output layer. One-month test data for each city was used, and these months data were not used for training. The results show that the maximum mean absolute percentage error (MAPE) was found to be 3.448% and the R-2 value 0.9987 for Polath. The best approach was found for Kirsehir (MAPE = 1.2257, R-2 = 0.9998). The MAPE and R-2 for the testing data were 3.3477 and 0.998534, respectively. The ANN models show greater accuracy for evaluating solar-resource possibilities in regions where a network of monitoring stations has not been established in Turkey. This study confirms the ability of the ANN to predict solar-radiation values precisely. (c) 2004 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.apenergy.2004.06.001
dc.identifier.endpage381en_US
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-10144220716
dc.identifier.scopusqualityQ1
dc.identifier.startpage367en_US
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2004.06.001
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3520
dc.identifier.volume80en_US
dc.identifier.wosWOS:000227858500003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectsolar-energy potentialen_US
dc.subjectcityen_US
dc.subjectTurkeyen_US
dc.subjectartificial neural-networken_US
dc.subjectformulationen_US
dc.titleSolar-energy potential in Turkeyen_US
dc.typeArticle

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