The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term

Wang, J; Lu, H; Dong, Y; Chi, D

The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term

Wang, J Lu, H

Dong, Y Chi, D

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Publication Type:: Journal Article
Citation:: Applied Mathematical Modelling, 2012, 36 (3), pp. 1184 - 1196
Issue Date:: 2012-03-01

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Dong, Y	en_US
dc.contributor.author	Chi, D	en_US
dc.date.issued	2012-03-01	en_US
dc.identifier.citation	Applied Mathematical Modelling, 2012, 36 (3), pp. 1184 - 1196	en_US
dc.identifier.issn	0307-904X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31248
dc.description.abstract	Within a competitive electric power market, electricity price is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of electricity price using chaotic sequences for forecasting of short term electricity price in the Australian power market. One modified model is applies seasonal adjustment and another modified model is employed seasonal adjustment and adaptive particle swarm optimization (APSO) that determines the parameters for the chaotic system. The experimental results show that the proposed methods performs noticeably better than the traditional chaotic algorithm. © 2011 Elsevier Inc.	en_US
dc.relation.ispartof	Applied Mathematical Modelling	en_US
dc.relation.isbasedon	10.1016/j.apm.2011.07.089	en_US
dc.subject.classification	Mechanical Engineering & Transports	en_US
dc.title	The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	36	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

Within a competitive electric power market, electricity price is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of electricity price using chaotic sequences for forecasting of short term electricity price in the Australian power market. One modified model is applies seasonal adjustment and another modified model is employed seasonal adjustment and adaptive particle swarm optimization (APSO) that determines the parameters for the chaotic system. The experimental results show that the proposed methods performs noticeably better than the traditional chaotic algorithm. © 2011 Elsevier Inc.

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http://hdl.handle.net/10453/31248