A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks

Basit Öğe Kaydı
dc.contributor.authorÇam, Ertuğrul
dc.contributor.authorArcaklıoğlu, Erol
dc.contributor.authorCavuşoğlu, Abdullah
dc.contributor.authorAkbıyık, Bilge
dc.date.accessioned2020-06-25T17:40:47Z
dc.date.available2020-06-25T17:40:47Z
dc.date.issued2005
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207; Cam, Ertugrul/0000-0001-6491-9225
dc.description.abstractIn this paper, average wind speed and wind power values are estimated using artificial neural networks (ANNs) in seven regions of Turkey. To start with, a network has been set up, and trained with the data set obtained from several stations-each station gather data from five different heights-from each region, one randomly selected height value of a station has been used as test data. Wind data readings corresponding to the last 50 years of relevant regions were obtained from the Turkish State Meteorological Service (TSMS). The software has been developed under Matlab 6.0. In the input layer, longitude, latitude, altitude, and height are used, while wind speeds and related power values correspond to output layer. Then we have used the networks to make predictions for varying heights, which are not incorporated to the system at the training stage. The network has successfully predicted the required output values for the test data and the mean error levels for regions differed between 3% and 6%. We believe that using ANNs average wind speed and wind power of a region can be predicted provided with lesser amount of sampling data, that the sampling mechanism is reliable and adequate. (C) 2004 Elsevier Ltd. All rights reserved.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.renene.2004.05.008
dc.identifier.endpage239en_US
dc.identifier.issn0960-1481
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-13944263195
dc.identifier.scopusqualityQ1
dc.identifier.startpage227en_US
dc.identifier.urihttps://doi.org/10.1016/j.renene.2004.05.008
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3549
dc.identifier.volume30en_US
dc.identifier.wosWOS:000224445200010
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.subjectwind speed predictionsen_US
dc.subjectartificial neural networksen_US
dc.subjectTurkeyen_US
dc.titleA classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networksen_US
dc.typeArticle

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