Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

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dc.contributor.authorKaytez, Fazil
dc.contributor.authorTaplamacioglu, M. Cengiz
dc.contributor.authorCam, Ertugrul
dc.contributor.authorHardalac, Firat
dc.date.accessioned2020-06-25T18:13:18Z
dc.date.available2020-06-25T18:13:18Z
dc.date.issued2015
dc.departmentKırıkkale Üniversitesi
dc.descriptionCam, Ertugrul/0000-0001-6491-9225
dc.description.abstractAccurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipMinistry of Energy and Natural Resources of Turkey (MENR), TURKSTAT, TETC, TEDCMinistry of Energy & Natural Resources - Turkeyen_US
dc.description.sponsorshipThe authors are grateful for the support provided for the present work by the Ministry of Energy and Natural Resources of Turkey (MENR), TURKSTAT, TETC, TEDC.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.ijepes.2014.12.036
dc.identifier.endpage438en_US
dc.identifier.issn0142-0615
dc.identifier.issn1879-3517
dc.identifier.scopus2-s2.0-84919800700
dc.identifier.scopusqualityQ1
dc.identifier.startpage431en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijepes.2014.12.036
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6174
dc.identifier.volume67en_US
dc.identifier.wosWOS:000348958800042
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofInternational Journal Of Electrical Power & Energy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectricity consumption forecastingen_US
dc.subjectRegression analysisen_US
dc.subjectArtificial neural networken_US
dc.subjectLeast square support vector machinesen_US
dc.titleForecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machinesen_US
dc.typeArticle

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