A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting

Song, J; Wang, J; Lu, H

A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting

Song, J Wang, J Lu, H

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Publication Type:: Journal Article
Citation:: Applied Energy, 2018, 215 pp. 643 - 658
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Song, J	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	Applied Energy, 2018, 215 pp. 643 - 658	en_US
dc.identifier.issn	0306-2619	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128162
dc.description.abstract	© 2018 Elsevier Ltd Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.	en_US
dc.relation.ispartof	Applied Energy	en_US
dc.relation.isbasedon	10.1016/j.apenergy.2018.02.070	en_US
dc.subject.classification	Energy	en_US
dc.title	A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	215	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	09 Engineering	en_US
utslib.for	14 Economics	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	215	en_US

Abstract:

© 2018 Elsevier Ltd Short-term wind speed forecasting has a significant influence on enhancing the operation efficiency and increasing the economic benefits of wind power generation systems. A substantial number of wind speed forecasting models, which are aimed at improving the forecasting performance, have been proposed. However, some conventional forecasting models do not consider the necessity and importance of data preprocessing. Moreover, they neglect the limitations of individual forecasting models, leading to poor forecasting accuracy. In this study, a novel model combining a data preprocessing technique, forecasting algorithms, an advanced optimization algorithm, and no negative constraint theory is developed. This combined model successfully overcomes some limitations of the individual forecasting models and effectively improves the forecasting accuracy. To estimate the effectiveness of the proposed combined model, 10-min wind speed data from the wind farm in Peng Lai, China are used as case studies. The experiment results demonstrate that the developed combined model is definitely superior compared to all other conventional models. Furthermore, it can be used as an effective technique for smart grid planning.

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http://hdl.handle.net/10453/128162