Wind power forecasting based on daily wind speed data using machine learning algorithms

dc.authoridGokcek, Murat/0000-0002-7951-4236
dc.authoridDemolli, Halil/0000-0001-6474-3549
dc.contributor.authorDemolli, Halil
dc.contributor.authorDokuz, Ahmet Sakir
dc.contributor.authorEcemis, Alper
dc.contributor.authorGokcek, Murat
dc.date.accessioned2024-11-07T13:25:16Z
dc.date.available2024-11-07T13:25:16Z
dc.date.issued2019
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractWind energy is a significant and eligible source that has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, long-term wind power forecasting was performed based on daily wind speed data using five machine learning algorithms. We proposed a method based on machine learning algorithms to forecast wind power values efficiently. We conducted several case studies to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, the results showed that machine learning-based models could be applied to a location different from model-trained locations. This study demonstrated that machine learning algorithms could be successfully used before the establishment of wind plants in an unknown geographical location whether it is logical by using the model of a base location.
dc.identifier.doi10.1016/j.enconman.2019.111823
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.scopus2-s2.0-85069657246
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2019.111823
dc.identifier.urihttps://hdl.handle.net/11480/14613
dc.identifier.volume198
dc.identifier.wosWOS:000491213400062
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEnergy Conversion and Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectWind energy
dc.subjectWind power forecasting
dc.subjectMachine Learning
dc.subjectRegression
dc.titleWind power forecasting based on daily wind speed data using machine learning algorithms
dc.typeArticle

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