Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning

Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-shor...

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Autores principales:	Hao Chen, Yngve Birkelund, Fuqing Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Machine learning Statistical comparison Turbulence Wind energy Wind forecast Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/77b7bce11f8f49fe8f7fcecee7b15360
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id	oai:doaj.org-article:77b7bce11f8f49fe8f7fcecee7b15360
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spelling	oai:doaj.org-article:77b7bce11f8f49fe8f7fcecee7b153602021-11-26T04:33:04ZExamination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning2352-484710.1016/j.egyr.2021.08.040https://doaj.org/article/77b7bce11f8f49fe8f7fcecee7b153602021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721006429https://doaj.org/toc/2352-4847Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power.Hao ChenYngve BirkelundFuqing YuanElsevierarticleMachine learningStatistical comparisonTurbulenceWind energyWind forecastElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 332-338 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Statistical comparison Turbulence Wind energy Wind forecast Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Machine learning Statistical comparison Turbulence Wind energy Wind forecast Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hao Chen Yngve Birkelund Fuqing Yuan Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
description	Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding wind turbulence, within three hours by employing several different machine learning algorithms. A rigorous and detailed statistical comparison of the predictions is conducted. The results show that the algorithms achieve reasonably accurate predictions, but turbulence intensity does not statistically contribute to wind power or speed forecasts. This observation illustrates the uncertainty of turbulence in wind power generation. Besides, differences between the types of algorithms for ultra-short-term wind forecasts are also statistically insignificant, demonstrating the unique stochasticity and complexity of wind speed and power.
format	article
author	Hao Chen Yngve Birkelund Fuqing Yuan
author_facet	Hao Chen Yngve Birkelund Fuqing Yuan
author_sort	Hao Chen
title	Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
title_short	Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
title_full	Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
title_fullStr	Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
title_full_unstemmed	Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
title_sort	examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/77b7bce11f8f49fe8f7fcecee7b15360
work_keys_str_mv	AT haochen examinationofturbulenceimpactsonultrashorttermwindpowerandspeedforecastswithmachinelearning AT yngvebirkelund examinationofturbulenceimpactsonultrashorttermwindpowerandspeedforecastswithmachinelearning AT fuqingyuan examinationofturbulenceimpactsonultrashorttermwindpowerandspeedforecastswithmachinelearning
_version_	1718409848974999552

Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning

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