Swarm intelligence approaches to estimate electricity energy demand in Turkey

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dc.contributor.authorKiran, Mustafa Servet
dc.contributor.authorOzceylan, Eren
dc.contributor.authorGunduz, Mesut
dc.contributor.authorPaksoy, Turan
dc.date.accessioned2020-03-26T18:31:27Z
dc.date.available2020-03-26T18:31:27Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper proposes two new models based on artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to estimate electricity energy demand in Turkey. ABC and PSO electricity energy estimation models (ABCEE and PSOEE) are developed by incorporating gross domestic product (GDP), population, import and export figures of Turkey as inputs. All models are proposed in two forms, linear and quadratic. Also different neighbor selection mechanisms are attempted for ABCEE model to increase convergence to minimum of the algorithm. In order to indicate the applicability and accuracy of the proposed models, a comparison is made with ant colony optimization (ACO) which is available for the same problem in the literature. According to obtained results, relative estimation errors of the proposed models are lower than ACO and quadratic form provides better-fit solutions than linear form due to fluctuations of the socio-economic indicators. Finally, Turkey's electricity energy demand is projected until 2025 according to three different scenarios. (C) 2012 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Scientific Research Project Fund (BAP)Selcuk Universityen_US
dc.description.sponsorshipWe are grateful for the comments by two anonymous referees on a previous draft of the paper and thank them for helping to improve the paper. In carrying out this research, the authors were supported by the Selcuk University Scientific Research Project Fund (BAP). These funds are hereby gratefully acknowledged. The authors are, of course, responsible for all the errors and the omissions.en_US
dc.identifier.doi10.1016/j.knosys.2012.06.009en_US
dc.identifier.endpage103en_US
dc.identifier.issn0950-7051en_US
dc.identifier.issn1872-7409en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage93en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.knosys.2012.06.009
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28447
dc.identifier.volume36en_US
dc.identifier.wosWOS:000311775200009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartofKNOWLEDGE-BASED SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAnt colony optimizationen_US
dc.subjectArtificial bee colonyen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectElectricity energy estimationen_US
dc.subjectSwarm intelligenceen_US
dc.titleSwarm intelligence approaches to estimate electricity energy demand in Turkeyen_US
dc.typeArticleen_US

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