Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
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, th...
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ftpubmed:oai:pubmedcentral.nih.gov:8027804 2023-05-15T14:52:33+02:00 Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic Chen, Hao Birkelund, Yngve Anfinsen, Stian Normann Staupe-Delgado, Reidar Yuan, Fuqing 2021-04-07 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027804/ https://doi.org/10.1038/s41598-021-87299-4 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027804/ http://dx.doi.org/10.1038/s41598-021-87299-4 © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. CC-BY Sci Rep Article Text 2021 ftpubmed https://doi.org/10.1038/s41598-021-87299-4 2021-04-11T00:41:17Z 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. Text Arctic PubMed Central (PMC) Arctic Scientific Reports 11 1 |
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Article Chen, Hao Birkelund, Yngve Anfinsen, Stian Normann Staupe-Delgado, Reidar Yuan, Fuqing Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic |
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Article |
description |
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 |
Text |
author |
Chen, Hao Birkelund, Yngve Anfinsen, Stian Normann Staupe-Delgado, Reidar Yuan, Fuqing |
author_facet |
Chen, Hao Birkelund, Yngve Anfinsen, Stian Normann Staupe-Delgado, Reidar Yuan, Fuqing |
author_sort |
Chen, Hao |
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 Publishing Group UK |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027804/ https://doi.org/10.1038/s41598-021-87299-4 |
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Arctic |
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Arctic |
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Arctic |
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Arctic |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027804/ http://dx.doi.org/10.1038/s41598-021-87299-4 |
op_rights |
© The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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CC-BY |
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https://doi.org/10.1038/s41598-021-87299-4 |
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Scientific Reports |
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