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dc.contributor.authorChen, Hao
dc.contributor.authorAnfinsen, Stian Normann
dc.contributor.authorBirkelund, Yngve
dc.contributor.authorYuan, Fuqing
dc.date.accessioned2021-11-26T09:30:49Z
dc.date.available2021-11-26T09:30:49Z
dc.date.issued2021-11
dc.description.abstractThe Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received sufficient research attention. In this paper, a framework for studying short-term wind speed volatility with statistical analysis and probabilistic modeling is constructed for an existing wind farm in Northern Norway. It is found that unlike the characteristics of wind power volatility, wind speed volatility cannot be described by the normal distribution. The reason is that even though the probability distribution of wind speed volatility is centrally symmetric, it is much more centrally concentrated and has thicker tails. After comparing three distributions corresponding to different sampling periods, this paper suggests utilizing the t distribution, with average modeling RMSE less than 0.006 and R<sup>2</sup> exceeding 0.995 and with the best modeling scenario of temporal resolution, the 30 mins has an RMSE of 0.0051 and an R<sup>2</sup> of 0.997, to more accurately and effectively explore the fluctuating characteristics of wind speed.en_US
dc.identifier.citationChen H, Anfinsen SN, Birkelund Y, Yuan F. Probability distributions for wind speed volatility characteristics: A case study of Northern Norway. Energy Reports. 2021en_US
dc.identifier.cristinIDFRIDAID 1956681
dc.identifier.doihttps://doi.org/10.1016/j.egyr.2021.07.125
dc.identifier.issn2352-4847
dc.identifier.urihttps://hdl.handle.net/10037/23177
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: 500::Environmental engineering: 610en_US
dc.subjectVDP::Teknologi: 500::Miljøteknologi: 610en_US
dc.titleProbability distributions for wind speed volatility characteristics: A case study of Northern Norwayen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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