A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction

Lv, M; Wang, J; Niu, X; Lu, H

A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction

Lv, M Wang, J Niu, X Lu, H

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Journal of Ambient Intelligence and Humanized Computing, 2022, pp. 1-20
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lv, M
dc.contributor.author	Wang, J
dc.contributor.author	Niu, X
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.date.accessioned	2023-03-09T23:21:38Z
dc.date.available	2023-03-09T23:21:38Z
dc.date.issued	2022-01-01
dc.identifier.citation	Journal of Ambient Intelligence and Humanized Computing, 2022, pp. 1-20
dc.identifier.issn	1868-5137
dc.identifier.issn	1868-5145
dc.identifier.uri	http://hdl.handle.net/10453/166920
dc.description.abstract	Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Journal of Ambient Intelligence and Humanized Computing
dc.relation.isbasedon	10.1007/s12652-021-03595-x
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s12652-021-03595-x
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing
dc.title	A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
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	embargoed	*
utslib.copyright.embargo	2024-07-01T00:00:00+1000Z
dc.date.updated	2023-03-09T23:21:37Z
pubs.publication-status	Published

Abstract:

Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting.

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http://hdl.handle.net/10453/166920