A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye

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dc.authoridEkici, Elanur/0000-0003-1919-5576
dc.authoridKoroglu, Tahsin/0000-0002-6587-3529
dc.contributor.authorKoroglu, Tahsin
dc.contributor.authorEkici, Elanur
dc.date.accessioned2025-01-06T17:44:50Z
dc.date.available2025-01-06T17:44:50Z
dc.date.issued2024
dc.description.abstractIn recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in canakkale Province, located in the northwest of Turkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network-genetic algorithm (ANN-GA) and ANN-particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.
dc.identifier.doi10.3390/app14031267
dc.identifier.issn2076-3417
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85192475444
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app14031267
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3201
dc.identifier.volume14
dc.identifier.wosWOS:001159982000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectwind speed estimation
dc.subjectartificial neural network
dc.subjectgenetic algorithm
dc.subjectparticle swarm optimization
dc.subjectweibull distribution
dc.subjectrayleigh distribution
dc.titleA Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye
dc.typeArticle

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