Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey

Basit Öğe Kaydı
dc.authoridGünay, M. Erdem/0000-0003-1282-718X
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.contributor.authorGunay, M. Erdem
dc.date.accessioned2024-07-18T20:42:36Z
dc.date.available2024-07-18T20:42:36Z
dc.date.issued2016
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, the annual gross electricity demand of Turkey was modeled by multiple linear regression and artificial neural networks as a function population, gross domestic product per capita, inflation percentage, unemployment percentage, average summer temperature and average winter temperature. Among these, the unemployment percentage and the average winter temperature were found to be insignificant to determine the demand for the years between 1975 and 2013. Next, the future values of the statistically significant variables were predicted by time series ANN models, and these were simulated in a multilayer perceptron ANN model to forecast the future annual electricity demand. The results were validated with a very high accuracy for the years that the electricity demand was known (20072013), and they were also superior to the official predictions (done by Ministry of Energy and Natural Resources of Turkey). The model was then used to forecast the annual gross electricity demand for the future years, and it was found that, the demand will be doubled reaching about 460 TW h in the year 2028. Finally, it was concluded that the approach applied in this work can easily be implemented for other countries to make accurate predictions for the future. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.enpol.2015.12.019
dc.identifier.endpage101en_US
dc.identifier.issn0301-4215
dc.identifier.issn1873-6777
dc.identifier.scopus2-s2.0-84956852398en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage92en_US
dc.identifier.urihttps://doi.org/10.1016/j.enpol.2015.12.019
dc.identifier.urihttps://hdl.handle.net/11411/7355
dc.identifier.volume90en_US
dc.identifier.wosWOS:000370104500009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofEnergy Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectTime Seriesen_US
dc.subjectElectricity Demand Forecastingen_US
dc.subjectPopulationen_US
dc.subjectEconomic İndicatorsen_US
dc.subjectAverage Ambient Temperatureen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectEnergy Demanden_US
dc.subjectConsumptionen_US
dc.subjectAlgorithmen_US
dc.subjectRegressionen_US
dc.subjectModelsen_US
dc.subjectIranen_US
dc.titleForecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey
dc.typeArticle

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications