Parameter estimation of the wind speed distribution model by dragonfly algorithm

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dc.authorscopusid58078215300
dc.authorscopusid55015750000
dc.authorscopusid58078724900
dc.contributor.authorKöse, Bayram
dc.contributor.authorAygün, Hilmi
dc.contributor.authorPak, Semih
dc.date.accessioned2023-03-22T19:47:51Z
dc.date.available2023-03-22T19:47:51Z
dc.date.issued2023
dc.departmentBelirleneceken_US
dc.description.abstractIn order to meet the increasing energy demand and to solve environmental problems, the interest in renewable energy sources continues with technology development studies and economic investments. Various methods are used to determine and estimate sustainable and renewable energy sources. Probability distribution functions are used in wind characterization and potential calculation of wind energy. In this study, the Dragonfly Algorithm (DA) is proposed to estimate the Weibull probability distribution function (Wpdf) parameters used in wind speed modeling and the two-component mixed Weibull distribution parameters used in modeling non-single peak wind speed data. The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Data obtained from 6 measurement stations were used in the study. According to the performance criteria, the two-component Weibull distribution was found to be more effective at all stations compared to the Weibull distribution model. It has been concluded that the proposed DA algorithm can be used effectively for parameter estimation in wind speed modeling. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.en_US
dc.description.sponsorshipThe authors have sent a request to KARES AVM, Electrical Engineer Basri GÜMÜŞ and Prof. Dr. Mehmet ÖZKAYMAK for his contributions.en_US
dc.identifier.doi10.17341/gazimmfd.935689
dc.identifier.endpage1756en_US
dc.identifier.issn13001884
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85146833057en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1747en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.935689
dc.identifier.urihttps://hdl.handle.net/20.500.14034/887
dc.identifier.volume38en_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherGazi Universitesien_US
dc.relation.journalJournal of the Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdragonfly algorithmen_US
dc.subjectestimationen_US
dc.subjectWeibull distribution parametersen_US
dc.subjectwind energy potentialen_US
dc.subjectDistribution functionsen_US
dc.subjectGenetic algorithmsen_US
dc.subjectInvestmentsen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMean square erroren_US
dc.subjectMethod of momentsen_US
dc.subjectParameter estimationen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectWind poweren_US
dc.subjectWind speeden_US
dc.subjectDistribution modelsen_US
dc.subjectDistribution parametersen_US
dc.subjectDragonfly algorithmen_US
dc.subjectParameters estimationen_US
dc.subjectProbability distribution functionsen_US
dc.subjectRenewable energy sourceen_US
dc.subjectTwo-componenten_US
dc.subjectWeibull distribution parameteren_US
dc.subjectWind energy potentialen_US
dc.subjectWind speed modelsen_US
dc.subjectWeibull distributionen_US
dc.titleParameter estimation of the wind speed distribution model by dragonfly algorithmen_US
dc.title.alternativeRüzgar hız dağılımı modelinin yusufcuk algoritması ile parametre tahminlemesi]en_US
dc.typeArticleen_US

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