The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey

Basit Öğe Kaydı
dc.contributor.authorGulcu, Saban
dc.contributor.authorKodaz, Halife
dc.date.accessioned2020-03-26T19:43:01Z
dc.date.available2020-03-26T19:43:01Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description8th International Conference on Advances in Information Technology (IAIT) -- DEC 19-22, 2016 -- Macau, PEOPLES R CHINAen_US
dc.description.abstractEnergy is the most important factor in improving the quality of life and advancing the economic and social progress. Demographic changes directly affect the energy demand. At present the worlds population is growing quickly. As of 2015, it was estimated at 7.3 billion. The population and the export of Turkey have been increasing for two decades. Consequently, electricity energy demand of Turkey has been increasing rapidly. This study aims to predict the future electricity energy demand of Turkey. In this paper, the prediction of the electricity demand of Turkey is modeled by using particle swarm optimization algorithm. The data of the gross domestic product, population, import and export are used as input data of the proposed model in the experiments. The GDP, import and export data are taken from the annual reports of the Turkish Ministry of Finance. The population data are taken from the Turkish Statistical Institute. The electricity demand data are taken from the Turkish Electricity Transmission Company. The statistical method R-2 and adjusted-R-2 are used as the performance criteria. The experimental results show that the generated model is very efficient. (c) 2017 The Authors. Published by Elsevier B.V.en_US
dc.identifier.doi10.1016/j.procs.2017.06.011en_US
dc.identifier.endpage70en_US
dc.identifier.issn1877-0509en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage64en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.procs.2017.06.011
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35580
dc.identifier.volume111en_US
dc.identifier.wosWOS:000418465800010en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartof8TH INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION TECHNOLOGYen_US
dc.relation.ispartofseriesProcedia Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectElectricity energy estimationen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectprediction of the future electricity demanden_US
dc.titleThe estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkeyen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu