Solar potential in Turkey

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dc.contributor.authorSözen, Adnan
dc.contributor.authorArcaklıoğlu, Erol
dc.date.accessioned2020-06-25T17:40:49Z
dc.date.available2020-06-25T17:40:49Z
dc.date.issued2005
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207
dc.description.abstractMost of the locations in Turkey receive abundant solar-energy, because Turkey lies in a sunny belt between 36degrees and 42 degreesN latitudes. Average annual temperature is 18 to 20 degreesC on the south coast, falls to 14-16 degreesC on the west coat, and fluctuates between 4 and 18 degreesC in the central parts. The yearly average solar-radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 It. In this study, a new formulation 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), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid (logsig) transfer function were used in the networks. Meteorological data for last four years (2000-2003) from 12 cities (Canakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Corum, Konya, Siirt, and Tekirdag) spread over Turkey were used in order to train the neural-network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine-duration, and mean temperature) are used in the input layer of the network. Solar-radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 3.832% and R-2 values to be about 99.9738% for the selected stations. The ANN models show greater accuracy for evaluating solar-resource posibilities 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 accurately. (C) 2004 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.apenergy.2004.02.003
dc.identifier.endpage45en_US
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-5044252418
dc.identifier.scopusqualityQ1
dc.identifier.startpage35en_US
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2004.02.003
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3559
dc.identifier.volume80en_US
dc.identifier.wosWOS:000225346000004
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 potential in Turkeyen_US
dc.typeArticle

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