Forecasting based on neural network approach of solar potential in Turkey

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dc.contributor.authorSözen, Adnan
dc.contributor.authorArcakhoğlu, Erol
dc.contributor.authorÖzalp, Mehmet
dc.contributor.authorÇağlar, Naci
dc.date.accessioned2020-06-25T17:40:37Z
dc.date.available2020-06-25T17:40:37Z
dc.date.issued2005
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAs Turkey lies near the sunny belt between 36 and 42 degrees N latitudes, most of the locations in Turkey receive abundant solar energy. Average annual temperature is 18-20 degrees C on the south coast, falls down to 14-16 degrees C on the west coast, and fluctuates 4-18 degrees C in the central parts. The yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to 2610 h. The main focus of this study is put forward to solar energy potential in Turkey using artificial neural networks (ANN's). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last 4 years (2000-2003) from 12 cities (Canakkale, Kars, Hakkari, Sakarya, Erzurum, Zonguldak, Balikesir, Artvin, Corum, Konya, Siirt, Tekirdag) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used as input to the network. Solar radiation is the output. The maximum mean absolute percentage error was found to be less than 6.78% and R 2 values to be about 99.7768% for the testing stations. These values were found to be 5.283 and 99.897% for the training stations. The trained and tested ANN models show greater accuracy for evaluating solar resource posibilities in regions where a network of monitoring stations have not been established in Turkey. The predictions from ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. (c) 2004 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.renene.2004.09.020
dc.identifier.endpage1090en_US
dc.identifier.issn0960-1481
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-12444263238
dc.identifier.scopusqualityQ1
dc.identifier.startpage1075en_US
dc.identifier.urihttps://doi.org/10.1016/j.renene.2004.09.020
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3503
dc.identifier.volume30en_US
dc.identifier.wosWOS:000227519900007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofRenewable 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.titleForecasting based on neural network approach of solar potential in Turkeyen_US
dc.typeArticle

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