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A review of short-term wind power generation forecasting methods in recent technological trends

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dc.contributor.authorSAĞLAM, ŞAFAK
dc.contributor.authorORAL, BÜLENT
dc.contributor.authorsArslan Tuncar E., Sağlam Ş., Oral B.
dc.date.accessioned2024-06-25T17:27:33Z
dc.date.available2024-06-25T17:27:33Z
dc.date.issued2024-12-01
dc.description.abstractClimate change and the escalating demand for energy are among the most pressing global challenges of our era. Renewable energy sources, such as wind energy, are considered a viable solution to these issues. However, the integration of renewable energy sources into electric power systems also presents operational challenges, particularly in terms of uncertainty. In order to mitigate this uncertainty, it is crucial to improve the accuracy of generation forecasting methods for wind energy. This review explores various wind power forecasting methods, categorizing them by factors such as time frame, and model structure. Special attention is given to short-term forecasting, crucial for the day-ahead electricity market. This study traces the evolution of wind power forecasting, from early statistical approaches to the integration of numerical weather prediction, machine learning, neural networks, and advanced techniques. Its aim is to provide valuable insights into wind power forecasting methods for stakeholders, including grid operators, traders, and wind farm operators. This review serves as a vital resource for researchers and industry professionals navigating the dynamic field of wind power forecasting, contributing to effective renewable energy resource management in a rapidly evolving energy sector.
dc.identifier.citationArslan Tuncar E., Sağlam Ş., Oral B., "A review of short-term wind power generation forecasting methods in recent technological trends", Energy Reports, cilt.12, ss.197-209, 2024
dc.identifier.doi10.1016/j.egyr.2024.06.006
dc.identifier.endpage209
dc.identifier.issn2352-4847
dc.identifier.startpage197
dc.identifier.urihttps://hdl.handle.net/11424/297077
dc.identifier.volume12
dc.language.isoeng
dc.relation.ispartofEnergy Reports
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectTarımsal Bilimler
dc.subjectZiraat
dc.subjectTarım Makineleri
dc.subjectTarımda Enerji
dc.subjectBiyoyakıt Teknolojisi
dc.subjectMühendislik ve Teknoloji
dc.subjectAgricultural Sciences
dc.subjectAgriculture
dc.subjectFarm Machinery
dc.subjectEnergy in Agriculture
dc.subjectBiofuels Technology
dc.subjectEngineering and Technology
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectENERJİ VE YAKITLAR
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectENGINEERING
dc.subjectENERGY & FUELS
dc.subjectGenel Enerji
dc.subjectFizik Bilimleri
dc.subjectGeneral Energy
dc.subjectPhysical Sciences
dc.subjectArtificial Neural Networks
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectNumerical Weather Prediction
dc.subjectShort Term
dc.subjectSupport Vector Machine
dc.subjectWind Energy Forecasting
dc.titleA review of short-term wind power generation forecasting methods in recent technological trends
dc.typearticle
dspace.entity.typePublication
local.avesis.idb9388e53-b048-48a1-8cd4-e11ea47493fc
local.indexed.atSCOPUS
relation.isAuthorOfPublication626969d4-2bdf-49c1-a214-cc9bf82c5b67
relation.isAuthorOfPublicationaf6e4989-cfff-4dcf-ad2e-9eca4a86cc9b
relation.isAuthorOfPublication.latestForDiscovery626969d4-2bdf-49c1-a214-cc9bf82c5b67

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