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Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea

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dc.contributor.authorKoo, Junmo-
dc.contributor.authorHan, Gwon Deok-
dc.contributor.authorChoi, Hyung Jong-
dc.contributor.authorShim, Joon Hyung-
dc.date.accessioned2021-09-04T09:17:22Z-
dc.date.available2021-09-04T09:17:22Z-
dc.date.created2021-06-18-
dc.date.issued2015-12-15-
dc.identifier.issn0360-5442-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/91575-
dc.description.abstractIn this study, we investigate the accuracy of wind-speed prediction at a designated target site using wind-speed data from reference stations that employ an ANN (artificial neural network). The reference and target sites fall into three geographical categories: plains, coast, and mountains of South Korea. Accurate wind-speed predictions are calculated by means of a correlation coefficient between the actual and simulated wind-speed data obtained by ANN. We investigate the effect of the geological characteristics of each category and the distance between reference and target sites on the accuracy of wind-speed prediction using ANN. (C) 2015 Elsevier Ltd. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectENERGY-
dc.subjectMECHANISMS-
dc.subjectPENINSULA-
dc.subjectSTATIONS-
dc.subjectMODEL-
dc.subjectPOWER-
dc.subjectAREA-
dc.titleWind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea-
dc.typeArticle-
dc.contributor.affiliatedAuthorShim, Joon Hyung-
dc.identifier.doi10.1016/j.energy.2015.10.026-
dc.identifier.scopusid2-s2.0-84954520579-
dc.identifier.wosid000367409500005-
dc.identifier.bibliographicCitationENERGY, v.93, pp.1296 - 1302-
dc.relation.isPartOfENERGY-
dc.citation.titleENERGY-
dc.citation.volume93-
dc.citation.startPage1296-
dc.citation.endPage1302-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaThermodynamics-
dc.relation.journalResearchAreaEnergy & Fuels-
dc.relation.journalWebOfScienceCategoryThermodynamics-
dc.relation.journalWebOfScienceCategoryEnergy & Fuels-
dc.subject.keywordPlusENERGY-
dc.subject.keywordPlusMECHANISMS-
dc.subject.keywordPlusPENINSULA-
dc.subject.keywordPlusSTATIONS-
dc.subject.keywordPlusMODEL-
dc.subject.keywordPlusPOWER-
dc.subject.keywordPlusAREA-
dc.subject.keywordAuthorWind energy-
dc.subject.keywordAuthorArtificial neural networks-
dc.subject.keywordAuthorWind prediction-
dc.subject.keywordAuthorClimate data-
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