Power load forecasting based on support vector machine and particle swarm optimization

Ren, G; Wen, S; Yan, Z; Hu, R; Zeng, Z; Cao, Y

Power load forecasting based on support vector machine and particle swarm optimization

Ren, G Wen, S Yan, Z

Hu, R Zeng, Z Cao, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2016, 2016-September, pp. 2003-2008
Issue Date:: 2016-09-27

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Field	Value	Language
dc.contributor.author	Ren, G
dc.contributor.author	Wen, S
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.contributor.author	Hu, R
dc.contributor.author	Zeng, Z
dc.contributor.author	Cao, Y
dc.date	2016-06-12
dc.date.accessioned	2023-02-23T04:24:55Z
dc.date.available	2023-02-23T04:24:55Z
dc.date.issued	2016-09-27
dc.identifier.citation	Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2016, 2016-September, pp. 2003-2008
dc.identifier.isbn	9781467384148
dc.identifier.uri	http://hdl.handle.net/10453/166387
dc.description.abstract	Accurate electric load forecasting is significant for the operation of the power systems and electricity markets. This paper proposes a particle swarm optimization with support vector machine (PSOSVM) to forecast annual power load. Based on radial basis function, support vector machine (SVM) is utilized to determine the structure and initial values of the parameters. Then, particle swarm optimization (PSO) is employed to optimize the parameters of the SVM model. In order to utilize the proposed method, practical data are divided into two parts, one is for training, the other is for testing. The combined method, PSOSVM, can effectively predict annual power load.
dc.language	en
dc.publisher	IEEE
dc.relation	National Natural Science Foundation of China61403152
dc.relation.ispartof	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
dc.relation.ispartof	12th World Congress on Intelligent Control and Automation (WCICA)
dc.relation.isbasedon	10.1109/WCICA.2016.7578535
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Power load forecasting based on support vector machine and particle swarm optimization
dc.type	Conference Proceeding
utslib.citation.volume	2016-September
utslib.location.activity	Guilin, PEOPLES R CHINA
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
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-23T04:24:54Z
pubs.finish-date	2016-06-15
pubs.publication-status	Published
pubs.start-date	2016-06-12
pubs.volume	2016-September

Abstract:

Accurate electric load forecasting is significant for the operation of the power systems and electricity markets. This paper proposes a particle swarm optimization with support vector machine (PSOSVM) to forecast annual power load. Based on radial basis function, support vector machine (SVM) is utilized to determine the structure and initial values of the parameters. Then, particle swarm optimization (PSO) is employed to optimize the parameters of the SVM model. In order to utilize the proposed method, practical data are divided into two parts, one is for training, the other is for testing. The combined method, PSOSVM, can effectively predict annual power load.

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http://hdl.handle.net/10453/166387