Using machine learning tools for forecasting natural gas consumption in the province of Istanbul

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dc.authorid0000-0002-9119-3252en_US
dc.contributor.authorBeyca, Ömer Faruk
dc.contributor.authorErvural, Beyzanur Çayır
dc.contributor.authorTatoğlu, Ekrem
dc.contributor.authorÖzuyar, Pınar Gökçin
dc.contributor.authorZaim, Selim
dc.contributor.authorTatoğlu, Ekrem
dc.contributor.authorZaim, Selim
dc.contributor.otherYönetim Bilimleri Fakültesi, İşletme Bölümü
dc.date.accessioned2019-03-23T11:42:32Z
dc.date.available2019-03-23T11:42:32Z
dc.date.issued2019
dc.departmentİHÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümüen_US
dc.description.abstractCommensurate with unprecedented increases in energy demand, awell-constructed forecastingmodel is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.en_US
dc.identifier.citationBeyca, Ö. F., Ervural, B. Ç., Tatoğlu, E., Özuyar, P. G., Zaim, S. (2019). Using machine learning tools for forecasting natural gas consumption in the province of Istanbul. Energy Economics, 80, pp. 937-949.en_US
dc.identifier.doi10.1016/j.eneco.2019.03.006en_US
dc.identifier.endpage949en_US
dc.identifier.issn0140-9883
dc.identifier.scopus2-s2.0-85063114902en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage937en_US
dc.identifier.urihttps://doi.org/10.1016/j.eneco.2019.03.006
dc.identifier.urihttps://hdl.handle.net/20.500.12154/667
dc.identifier.volume80en_US
dc.identifier.wosWOS:000474681100068en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorTatoğlu, Ekrem
dc.institutionauthorid0000-0002-9119-3252en_US
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.ihupublicationcategory114en_US
dc.relation.ispartofEnergy Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectNatural Gas Forecastingen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Neural Networken_US
dc.subjectSupport Vector Regressionen_US
dc.subjectEmerging Countries Istanbulen_US
dc.titleUsing machine learning tools for forecasting natural gas consumption in the province of Istanbulen_US
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication29f46236-65bf-4da8-b2be-b9826daecb5e
relation.isAuthorOfPublicatione854a5d5-11a9-4148-a1aa-863a4c13eeb5
relation.isAuthorOfPublication.latestForDiscovery29f46236-65bf-4da8-b2be-b9826daecb5e
relation.isOrgUnitOfPublicationc9253b76-6094-4836-ac99-2fcd5392d68f
relation.isOrgUnitOfPublication.latestForDiscoveryc9253b76-6094-4836-ac99-2fcd5392d68f

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