Short Term Load Forecasting for Turkey Energy Distribution System with Artificial Neural Networks

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dc.contributor.authorTosun, Salih
dc.contributor.authorÖztürk, Ali
dc.contributor.authorTaşpınar, Fatih
dc.date.accessioned2020-04-30T23:31:56Z
dc.date.available2020-04-30T23:31:56Z
dc.date.issued2019
dc.departmentDÜ, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS: 000499332300003en_US
dc.description.abstractThe constant increase in consumption of electricity has become one of the biggest problems today. The evaluation of energy resources has also made it worthwhile to consume it. In this respect, the transmission of electric energy and the operation of power systems have become important issues. As a result, reliable, high quality and affordable energy supply has become the most important task of operators. Realizing these elements can certainly be accomplished with good planning. One of the most important elements of this planning is undoubtedly consumption estimates. Therefore, knowing when consumers will consume energy is of great importance for operators as well as energy producers. Consumption estimates or, in other words, load estimates are also important in terms of the price balance that will occur in the market. In this study, the short-term load estimation of Duzce, Turkey is performed with Artificial Neural Networks (ANN). In the study, the April values were taken as reference and the estimates were obtained according to the input results of this month. As a result of this study, it is seen that the load consumption with nonlinear data can be successfully forecasted by ANN.en_US
dc.description.sponsorshipDuzce UniversityDuzce Universityen_US
dc.description.sponsorshipThis study was carried out in cooperation with SEDAS and Duzce University within the scope of "Influence of Optimization Method on Uncertainty Costs of Profile Coefficients Methodology and Optimization Project" supported by EPDK on May 28.en_US
dc.identifier.doi10.17559/TV-20180814115917en_US
dc.identifier.endpage1553en_US
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1545en_US
dc.identifier.urihttps://doi.org/10.17559/TV-20180814115917
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4536
dc.identifier.volume26en_US
dc.identifier.wosWOS:000499332300003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherUniv Osijek, Tech Facen_US
dc.relation.ispartofTehnicki Vjesnik-Technical Gazetteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectElectric Energyen_US
dc.subjectShort-Term Load Forecastingen_US
dc.titleShort Term Load Forecasting for Turkey Energy Distribution System with Artificial Neural Networksen_US
dc.typeArticleen_US

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