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Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data

In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy so...

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Detalles Bibliográficos
Autores principales: Wacker, Benjamin, Schlüter, Jan Chr.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563481/
https://www.ncbi.nlm.nih.gov/pubmed/34754770
http://dx.doi.org/10.1016/j.mex.2021.101499
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author Wacker, Benjamin
Schlüter, Jan Chr.
author_facet Wacker, Benjamin
Schlüter, Jan Chr.
author_sort Wacker, Benjamin
collection PubMed
description In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy source. As combinations of arbitrary power curve modeling techniques and arbitrary wind speed distributions based on wind speed data are seldom combined, the abstract combination of these two aspects in wind power output generation prediction in one pipeline is thoroughly described here. Conclusively, one detailed example wind speed data set from a weather station situation in Bremen, Germany illustrates applicability of the presented framework.
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spelling pubmed-85634812021-11-08 Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data Wacker, Benjamin Schlüter, Jan Chr. MethodsX Method Article In this article, an abstract framework for annual averaged wind power output generation prediction of wind turbines is presented which is heavily based on large wind speed data sets and power curve data of wind turbines due to the rising interest in wind energy as one main future renewable energy source. As combinations of arbitrary power curve modeling techniques and arbitrary wind speed distributions based on wind speed data are seldom combined, the abstract combination of these two aspects in wind power output generation prediction in one pipeline is thoroughly described here. Conclusively, one detailed example wind speed data set from a weather station situation in Bremen, Germany illustrates applicability of the presented framework. Elsevier 2021-08-25 /pmc/articles/PMC8563481/ /pubmed/34754770 http://dx.doi.org/10.1016/j.mex.2021.101499 Text en © 2021 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Method Article
Wacker, Benjamin
Schlüter, Jan Chr.
Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title_full Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title_fullStr Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title_full_unstemmed Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title_short Pipeline for Annual Averaged Wind Power Output Generation Prediction of Wind Turbines Based on Large Wind Speed Data Sets and Power Curve Data
title_sort pipeline for annual averaged wind power output generation prediction of wind turbines based on large wind speed data sets and power curve data
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563481/
https://www.ncbi.nlm.nih.gov/pubmed/34754770
http://dx.doi.org/10.1016/j.mex.2021.101499
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