Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/4837
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dc.contributor.authorÖztürk, Harun Kemal-
dc.contributor.authorCeylan, Halim-
dc.date.accessioned2019-08-16T11:37:50Z
dc.date.available2019-08-16T11:37:50Z
dc.date.issued2005-
dc.identifier.issn0363-907X-
dc.identifier.urihttps://hdl.handle.net/11499/4837-
dc.identifier.urihttps://doi.org/10.1002/er.1092-
dc.description.abstractThis study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best-fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. 'High- and low-growth scenarios' are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Energy Researchen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectricity consumptionen_US
dc.subjectElectricity projectionen_US
dc.subjectGenetic algorithmen_US
dc.subjectTurkey's electricityen_US
dc.subjectEconomicsen_US
dc.subjectElectric power utilizationen_US
dc.subjectError analysisen_US
dc.subjectGenetic algorithmsen_US
dc.subjectMathematical modelsen_US
dc.subjectProject managementen_US
dc.subjectGenetic algorithm electricity demand modelen_US
dc.subjectGross national producten_US
dc.subjectElectric load managementen_US
dc.subjectelectricityen_US
dc.subjectEastern Hemisphereen_US
dc.subjectEurasiaen_US
dc.subjectTurkeyen_US
dc.subjectWorlden_US
dc.titleForecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case studyen_US
dc.typeArticleen_US
dc.identifier.volume29en_US
dc.identifier.issue9en_US
dc.identifier.startpage829
dc.identifier.startpage829en_US
dc.identifier.endpage840en_US
dc.authorid0000-0003-4831-1118-
dc.identifier.doi10.1002/er.1092-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-21844460262en_US
dc.identifier.wosWOS:000230211500003en_US
dc.identifier.scopusqualityQ1-
dc.ownerPamukkale_University-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.grantfulltextnone-
crisitem.author.dept10.07. Mechanical Engineering-
crisitem.author.dept10.02. Civil Engineering-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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