A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting

Wang, J; An, Y; Li, Z; Lu, H

A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting

Wang, J An, Y Li, Z Lu, H

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energy, 2022, 251, pp. 123960
Issue Date:: 2022-07

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	An, Y
dc.contributor.author	Li, Z
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.date.accessioned	2023-03-09T04:57:27Z
dc.date.available	2023-03-09T04:57:27Z
dc.date.issued	2022-07
dc.identifier.citation	Energy, 2022, 251, pp. 123960
dc.identifier.issn	0360-5442
dc.identifier.uri	http://hdl.handle.net/10453/166796
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Energy
dc.relation.isbasedon	10.1016/j.energy.2022.123960
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.title	A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting
dc.type	Journal Article
utslib.citation.volume	251
utslib.for	0913 Mechanical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-07-01T00:00:00+1000Z
dc.date.updated	2023-03-09T04:57:17Z
pubs.publication-status	Published
pubs.volume	251

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