A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

Su, Z; Wang, J; Lu, H; Zhao, G

A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

Su, Z Wang, J Lu, H

Zhao, G

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Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2014, 85 pp. 443 - 452
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Su, Z	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Zhao, G	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Energy Conversion and Management, 2014, 85 pp. 443 - 452	en_US
dc.identifier.issn	0196-8904	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43439
dc.description.abstract	Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily. © 2014 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Energy Conversion and Management	en_US
dc.relation.isbasedon	10.1016/j.enconman.2014.05.058	en_US
dc.subject.classification	Energy	en_US
dc.title	A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	85	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	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.publication-status	Published	en_US
pubs.volume	85	en_US

Abstract:

Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily. © 2014 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/43439