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dc.contributor.authorDemirel, Ö. F.
dc.contributor.authorZaim, S.
dc.contributor.authorÇalışkan, A.
dc.contributor.authorÖzuyar, Pınar Gökçin
dc.date.accessioned2016-07-26T12:21:48Z
dc.date.available2016-07-26T12:21:48Z
dc.date.issued2012
dc.identifier.issn1303-6203
dc.identifier.urihttp://hdl.handle.net/10679/4290
dc.identifier.urihttps://journals.tubitak.gov.tr/elektrik/abstract.htm?id=12919
dc.description.abstractThe fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a result, if the forecasts are not done correctly, either unused natural gas amounts must be paid or there will be shortages of natural gas in the planning periods.
dc.language.isoengen_US
dc.publisherTÜBİTAK
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.rightsopenAccess
dc.titleForecasting natural gas consumption in Istanbul using neural networks and multivariate time series methodsen_US
dc.typeArticleen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-2505-2216 & YÖK ID 148993) Özuyar, Pınar
dc.contributor.ozuauthorÖzuyar, Pınar Gökçin
dc.identifier.volume20
dc.identifier.issue5
dc.identifier.startpage695
dc.identifier.endpage711
dc.identifier.wosWOS:000306302000004
dc.identifier.doi10.3906/elk-1101-1029
dc.subject.keywordsForecasting
dc.subject.keywordsNeural networks
dc.subject.keywordsNatural gas
dc.subject.keywordsTime series
dc.identifier.scopusSCOPUS:2-s2.0-84861810597
dc.contributor.authorFemale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institution Academic Staff


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