The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence

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dc.authoridAksu, Inayet Ozge/0000-0002-0963-2982
dc.authoridDemirdelen, Tugce/0000-0002-1602-7262
dc.authoridTekin, Piril/0000-0002-2326-7571
dc.authoridekinci, firat/0000-0002-4888-7881
dc.contributor.authorDemirdelen, Tugce
dc.contributor.authorTekin, Piril
dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorEkinci, Firat
dc.date.accessioned2025-01-06T17:43:50Z
dc.date.available2025-01-06T17:43:50Z
dc.date.issued2019
dc.description.abstractIn order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters' estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.
dc.description.sponsorshipScientific Project Unit of Adana Alparslan Turkes Science and Technology University [BAP-19103005]
dc.description.sponsorshipThe Scientific Project Unit of Adana Alparslan Turkes Science and Technology University (BAP-19103005).
dc.identifier.doi10.3390/su11174803
dc.identifier.issn2071-1050
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85071993412
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su11174803
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2829
dc.identifier.volume11
dc.identifier.wosWOS:000486877700291
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectoptimization
dc.subjectwind energy
dc.subjectwind turbine optimized model
dc.subjectwind turbine parameter prediction
dc.subjectfirefly algorithm
dc.titleThe Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence
dc.typeArticle

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