Forecasting electricity consumption of OECD countries: A global machine learning modeling approach

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dc.authoridGunay, M. Erdem/0000-0003-1282-718X|Sen, Doruk/0000-0003-3353-5952
dc.authorwosidSen, Doruk/D-4547-2016
dc.contributor.authorSen, Doruk
dc.contributor.authorTunc, K. M. Murat
dc.contributor.authorGunay, M. Erdem
dc.date.accessioned2024-07-18T20:58:30Z
dc.date.available2024-07-18T20:58:30Z
dc.date.issued2021
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractElectricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.en_US
dc.identifier.doi10.1016/j.jup.2021.101222
dc.identifier.issn0957-1787
dc.identifier.issn1878-4356
dc.identifier.scopus2-s2.0-85105546568en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jup.2021.101222
dc.identifier.urihttps://hdl.handle.net/11411/8988
dc.identifier.volume70en_US
dc.identifier.wosWOS:000658799200003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofUtilities Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Neural Networken_US
dc.subjectSupport Vector Machineen_US
dc.subjectNatural-Gas Consumptionen_US
dc.subjectEnergy-Consumptionen_US
dc.subjectTime-Seriesen_US
dc.subjectSocioeconomic Indicatorsen_US
dc.subjectNeural-Networken_US
dc.subjectDemanden_US
dc.subjectCombinationen_US
dc.subjectRegressionen_US
dc.subjectArimaen_US
dc.titleForecasting electricity consumption of OECD countries: A global machine learning modeling approachen_US
dc.typeArticleen_US

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