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dc.contributor.authorChen, Hao
dc.contributor.authorBirkelund, Yngve
dc.contributor.authorYuan, Fuqing
dc.date.accessioned2021-11-29T08:23:13Z
dc.date.available2021-11-29T08:23:13Z
dc.date.issued2021-11-25
dc.description.abstractWind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power.en_US
dc.identifier.citationChen H, Birkelund Y, Yuan F. Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning. Energy Reports. 2021en_US
dc.identifier.cristinIDFRIDAID 1959529
dc.identifier.doihttps://doi.org/10.1016/j.egyr.2021.08.040
dc.identifier.issn2352-4847
dc.identifier.urihttps://hdl.handle.net/10037/23188
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofChen, H. (2022). Data-driven Arctic wind energy analysis by statistical and machine learning approaches. (Doctoral thesis). <a href=https://hdl.handle.net/10037/26938>https://hdl.handle.net/10037/26938</a>
dc.relation.journalEnergy Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleExamination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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