Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

Xing, Q; Wang, J; Lu, H; Wang, S

Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast

Xing, Q Wang, J Lu, H

Wang, S

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2022, 263
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Xing, Q
dc.contributor.author	Wang, J
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.contributor.author	Wang, S
dc.date.accessioned	2023-03-09T02:48:31Z
dc.date.available	2023-03-09T02:48:31Z
dc.date.issued	2022-07-01
dc.identifier.citation	Energy Conversion and Management, 2022, 263
dc.identifier.issn	0196-8904
dc.identifier.issn	1879-2227
dc.identifier.uri	http://hdl.handle.net/10453/166763
dc.description.abstract	Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2022.115583
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.title	Research of a novel short-term wind forecasting system based on multi-objective Aquila optimizer for point and interval forecast
dc.type	Journal Article
utslib.citation.volume	263
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical 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	2023-03-09T02:48:10Z
pubs.publication-status	Published
pubs.volume	263

Abstract:

Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.

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http://hdl.handle.net/10453/166763