Combined modeling for electric load forecasting with adaptive particle swarm optimization

Wang, J; Zhu, S; Zhang, W; Lu, H

Combined modeling for electric load forecasting with adaptive particle swarm optimization

Wang, J Zhu, S Zhang, W Lu, H

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Publication Type:: Journal Article
Citation:: Energy, 2010, 35 (4), pp. 1671 - 1678
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhu, S	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Energy, 2010, 35 (4), pp. 1671 - 1678	en_US
dc.identifier.issn	0360-5442	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13056
dc.description.abstract	Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising. © 2009 Elsevier Ltd.	en_US
dc.relation.ispartof	Energy	en_US
dc.relation.isbasedon	10.1016/j.energy.2009.12.015	en_US
dc.subject.classification	Energy	en_US
dc.title	Combined modeling for electric load forecasting with adaptive particle swarm optimization	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	35	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
dc.location.activity	ISI:000277218100020	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	35	en_US

Abstract:

Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising. © 2009 Elsevier Ltd.

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