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dc.contributor.authorChen, Hao
dc.date.accessioned2021-07-06T08:35:05Z
dc.date.available2021-07-06T08:35:05Z
dc.date.issued2021-04-07
dc.description.abstractMapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density functions are used to model wind speed for five wind parks in the Norwegian Arctic region. A comparison between wind speed data from numerical weather prediction models and measurements is made, and a probability analysis for the wind speed interval corresponding to the rated power, which is largely absent in the existing literature, is presented. The results of the present study suggest that no single probability function outperforms across all scenarios. However, some differences emerged from the models when applied to different wind parks. The Nakagami and Generalised extreme value distributions were chosen for the numerical weather predicted prediction and the observed wind speed modelling, respectively, due to their superiority and stability compared with other methods. This paper, therefore, provides a novel direction for understanding the numerical weather prediction wind model and shows that its speed statistical features are better captured than those of real wind.en_US
dc.identifier.citationChen H. Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic. Scientific Reports. 2021en_US
dc.identifier.cristinIDFRIDAID 1902785
dc.identifier.doi10.1038/s41598-021-87299-4
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/21754
dc.language.isoengen_US
dc.publisherNature Researchen_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.journalScientific 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.titleAssessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arcticen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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