A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

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dc.contributor.authorKiran, Mustafa Servet
dc.contributor.authorOzceylan, Eren
dc.contributor.authorGunduz, Mesut
dc.contributor.authorPaksoy, Turan
dc.date.accessioned2020-03-26T18:23:35Z
dc.date.available2020-03-26T18:23:35Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThe authors express their gratitude to the three anonymous reviewers for their valuable comments on the paper. In carrying out this research, the authors have been supported by the Selcuk University Scientific Research Project Fund (BAP), and these funds are hereby gratefully acknowledged.en_US
dc.identifier.doi10.1016/j.enconman.2011.08.004en_US
dc.identifier.endpage83en_US
dc.identifier.issn0196-8904en_US
dc.identifier.issn1879-2227en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage75en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.enconman.2011.08.004
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27686
dc.identifier.volume53en_US
dc.identifier.wosWOS:000297891700009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofENERGY CONVERSION AND MANAGEMENTen_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.subjectEnergy demanden_US
dc.subjectEstimationen_US
dc.subjectHybrid meta-heuristicen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectTurkeyen_US
dc.titleA novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkeyen_US
dc.typeArticleen_US

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