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

Basit Öğe Kaydı
dc.authorid0000-0002-9119-3252
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 Bölümü
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.
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.
dc.identifier.doi10.1016/j.eneco.2019.03.006
dc.identifier.endpage949
dc.identifier.issn0140-9883
dc.identifier.scopus2-s2.0-85063114902
dc.identifier.scopusqualityQ1
dc.identifier.startpage937
dc.identifier.urihttps://doi.org/10.1016/j.eneco.2019.03.006
dc.identifier.urihttps://hdl.handle.net/20.500.12154/667
dc.identifier.volume80
dc.identifier.wosWOS:000474681100068
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorTatoğlu, Ekrem
dc.institutionauthorid0000-0002-9119-3252
dc.language.isoen
dc.publisherElsevier
dc.relation.ihupublicationcategory114
dc.relation.ispartofEnergy Economics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectNatural Gas Forecasting
dc.subjectMachine Learning
dc.subjectArtificial Neural Network
dc.subjectSupport Vector Regression
dc.subjectEmerging Countries Istanbul
dc.titleUsing machine learning tools for forecasting natural gas consumption in the province of Istanbul
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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