Short-term wind power prediction with harmony search algorithm: Belen region

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dc.contributor.authorEşsiz, Esra Saraç
dc.date.accessioned2025-01-06T17:30:24Z
dc.date.available2025-01-06T17:30:24Z
dc.date.issued2022
dc.description.abstractWind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction. © Author(s) 2022.
dc.identifier.doi10.31127/tuje.970959
dc.identifier.endpage255
dc.identifier.issn2587-1366
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85167460562
dc.identifier.scopusqualityQ3
dc.identifier.startpage251
dc.identifier.trdizinid1114471
dc.identifier.urihttps://doi.org/10.31127/tuje.970959
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1114471
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1584
dc.identifier.volume6
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherMurat Yakar
dc.relation.ispartofTurkish Journal of Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectArtificial neural networks
dc.subjectFeature Selection
dc.subjectRenewable Energy
dc.subjectShort-term forecast
dc.subjectWind power
dc.titleShort-term wind power prediction with harmony search algorithm: Belen region
dc.typeArticle

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