Hybrid forecasting model-based data mining and genetic algorithm-Adaptive particle swarm optimisation: A case study of wind speed time series

Wang, J; Zhang, F; Liu, F; Ma, J

Hybrid forecasting model-based data mining and genetic algorithm-Adaptive particle swarm optimisation: A case study of wind speed time series

Wang, J Zhang, F Liu, F

Ma, J

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Publisher:: INST ENGINEERING TECHNOLOGY-IET
Publication Type:: Journal Article
Citation:: IET Renewable Power Generation, 2016, 10, (3), pp. 287-298
Issue Date:: 2016-03-01

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Zhang, F
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.contributor.author	Ma, J
dc.date.accessioned	2022-08-13T21:32:41Z
dc.date.available	2022-08-13T21:32:41Z
dc.date.issued	2016-03-01
dc.identifier.citation	IET Renewable Power Generation, 2016, 10, (3), pp. 287-298
dc.identifier.issn	1752-1416
dc.identifier.issn	1752-1424
dc.identifier.uri	http://hdl.handle.net/10453/160100
dc.description.abstract	Wind energy has been part of the fastest growing renewable energy sources and is clean and pollution-free. Wind energy has been gaining increasing global attention, and wind speed forecasting plays a vital role in the wind energy field. However, such forecasting has been demonstrated to be a challenging task due to the effect of various meteorological factors. This study proposes a hybrid forecasting model that can effectively provide preprocessing for the original data and improve forecasting accuracy. The developed model applies a genetic algorithm-Adaptive particle swarm optimisation algorithm to optimise the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined in regard to the wind farms of eastern China. The forecasting performance demonstrates that the developed model is better than some traditional models (for example, back propagation, WNN, fuzzy neural network, and support vector machine), and its applicability is further verified by the paired-sample T tests.
dc.language	English
dc.publisher	INST ENGINEERING TECHNOLOGY-IET
dc.relation.ispartof	IET Renewable Power Generation
dc.relation.isbasedon	10.1049/iet-rpg.2015.0010
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.title	Hybrid forecasting model-based data mining and genetic algorithm-Adaptive particle swarm optimisation: A case study of wind speed time series
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-13T21:32:40Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	3

Abstract:

Wind energy has been part of the fastest growing renewable energy sources and is clean and pollution-free. Wind energy has been gaining increasing global attention, and wind speed forecasting plays a vital role in the wind energy field. However, such forecasting has been demonstrated to be a challenging task due to the effect of various meteorological factors. This study proposes a hybrid forecasting model that can effectively provide preprocessing for the original data and improve forecasting accuracy. The developed model applies a genetic algorithm-Adaptive particle swarm optimisation algorithm to optimise the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined in regard to the wind farms of eastern China. The forecasting performance demonstrates that the developed model is better than some traditional models (for example, back propagation, WNN, fuzzy neural network, and support vector machine), and its applicability is further verified by the paired-sample T tests.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/160100