Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic

Abstract Mapping 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 a...

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Autores principales:	Hao Chen, Yngve Birkelund, Stian Normann Anfinsen, Reidar Staupe-Delgado, Fuqing Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f35c4dab699545dcb7270426635f61ab
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spelling	oai:doaj.org-article:f35c4dab699545dcb7270426635f61ab2021-12-02T14:17:27ZAssessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic10.1038/s41598-021-87299-42045-2322https://doaj.org/article/f35c4dab699545dcb7270426635f61ab2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87299-4https://doaj.org/toc/2045-2322Abstract Mapping 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.Hao ChenYngve BirkelundStian Normann AnfinsenReidar Staupe-DelgadoFuqing YuanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hao Chen Yngve Birkelund Stian Normann Anfinsen Reidar Staupe-Delgado Fuqing Yuan Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
description	Abstract Mapping 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.
format	article
author	Hao Chen Yngve Birkelund Stian Normann Anfinsen Reidar Staupe-Delgado Fuqing Yuan
author_facet	Hao Chen Yngve Birkelund Stian Normann Anfinsen Reidar Staupe-Delgado Fuqing Yuan
author_sort	Hao Chen
title	Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
title_short	Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
title_full	Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
title_fullStr	Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
title_full_unstemmed	Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
title_sort	assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the arctic
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f35c4dab699545dcb7270426635f61ab
work_keys_str_mv	AT haochen assessingprobabilisticmodellingforwindspeedfromnumericalweatherpredictionmodelandobservationinthearctic AT yngvebirkelund assessingprobabilisticmodellingforwindspeedfromnumericalweatherpredictionmodelandobservationinthearctic AT stiannormannanfinsen assessingprobabilisticmodellingforwindspeedfromnumericalweatherpredictionmodelandobservationinthearctic AT reidarstaupedelgado assessingprobabilisticmodellingforwindspeedfromnumericalweatherpredictionmodelandobservationinthearctic AT fuqingyuan assessingprobabilisticmodellingforwindspeedfromnumericalweatherpredictionmodelandobservationinthearctic
_version_	1718391641667010560

Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic

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