Application of machine learning for wind energy from design to energy-Water nexus: A Survey

The world’s dependency on fossil fuels is decreasing swiftly, and countries rely more on renewable energies. Among renewable energies, wind energy has become one of the most significant ones. Increasing its production and reducing energy and water costs has attracted many attentions. Moreover, due t...

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Autores principales:	Farhad Elyasichamazkoti, Abolhasan Khajehpoor
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Renewable energy Wind energy Artificial neural network Artificial intelligence Renewable energy sources TJ807-830 Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/f695102e28c7489e83f6be987849c3f1
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spelling	oai:doaj.org-article:f695102e28c7489e83f6be987849c3f12021-12-02T05:04:52ZApplication of machine learning for wind energy from design to energy-Water nexus: A Survey2772-427110.1016/j.nexus.2021.100011https://doaj.org/article/f695102e28c7489e83f6be987849c3f12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2772427121000115https://doaj.org/toc/2772-4271The world’s dependency on fossil fuels is decreasing swiftly, and countries rely more on renewable energies. Among renewable energies, wind energy has become one of the most significant ones. Increasing its production and reducing energy and water costs has attracted many attentions. Moreover, due to the uncertainty of wind speed, power generation of wind farms is facing high volatility, which is affecting the electricity markets. Thus, in light of the rapid growth of wind energy technologies, new approaches based on advanced analytic are required. This paper presents a comprehensive review of artificial intelligence employed in wind energy systems, surveying the studies most applied in various applications and resulting from artificial neural networks (ANN) could be a sustainable approach instead of conventional methods in many cases. A large number of research studies associated with this topic are published since 2015, and based on the application could be categorized in five main groups: wind speed prediction, design optimization, fault detection, optimal control and maintenance planning. A statistical analysis of ANN application in these fields is carried out for the present time and future trends.Farhad ElyasichamazkotiAbolhasan KhajehpoorElsevierarticleRenewable energyWind energyArtificial neural networkArtificial intelligenceRenewable energy sourcesTJ807-830Agriculture (General)S1-972ENEnergy Nexus, Vol 2, Iss , Pp 100011- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Renewable energy Wind energy Artificial neural network Artificial intelligence Renewable energy sources TJ807-830 Agriculture (General) S1-972
spellingShingle	Renewable energy Wind energy Artificial neural network Artificial intelligence Renewable energy sources TJ807-830 Agriculture (General) S1-972 Farhad Elyasichamazkoti Abolhasan Khajehpoor Application of machine learning for wind energy from design to energy-Water nexus: A Survey
description	The world’s dependency on fossil fuels is decreasing swiftly, and countries rely more on renewable energies. Among renewable energies, wind energy has become one of the most significant ones. Increasing its production and reducing energy and water costs has attracted many attentions. Moreover, due to the uncertainty of wind speed, power generation of wind farms is facing high volatility, which is affecting the electricity markets. Thus, in light of the rapid growth of wind energy technologies, new approaches based on advanced analytic are required. This paper presents a comprehensive review of artificial intelligence employed in wind energy systems, surveying the studies most applied in various applications and resulting from artificial neural networks (ANN) could be a sustainable approach instead of conventional methods in many cases. A large number of research studies associated with this topic are published since 2015, and based on the application could be categorized in five main groups: wind speed prediction, design optimization, fault detection, optimal control and maintenance planning. A statistical analysis of ANN application in these fields is carried out for the present time and future trends.
format	article
author	Farhad Elyasichamazkoti Abolhasan Khajehpoor
author_facet	Farhad Elyasichamazkoti Abolhasan Khajehpoor
author_sort	Farhad Elyasichamazkoti
title	Application of machine learning for wind energy from design to energy-Water nexus: A Survey
title_short	Application of machine learning for wind energy from design to energy-Water nexus: A Survey
title_full	Application of machine learning for wind energy from design to energy-Water nexus: A Survey
title_fullStr	Application of machine learning for wind energy from design to energy-Water nexus: A Survey
title_full_unstemmed	Application of machine learning for wind energy from design to energy-Water nexus: A Survey
title_sort	application of machine learning for wind energy from design to energy-water nexus: a survey
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f695102e28c7489e83f6be987849c3f1
work_keys_str_mv	AT farhadelyasichamazkoti applicationofmachinelearningforwindenergyfromdesigntoenergywaternexusasurvey AT abolhasankhajehpoor applicationofmachinelearningforwindenergyfromdesigntoenergywaternexusasurvey
_version_	1718400628595621888

Application of machine learning for wind energy from design to energy-Water nexus: A Survey

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