Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

Guo, Z; Zhao, W; Lu, H; Wang, J

Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

Guo, Z Zhao, W Lu, H

Wang, J

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Publication Type:: Journal Article
Citation:: Renewable Energy, 2012, 37 (1), pp. 241 - 249
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Guo, Z	en_US
dc.contributor.author	Zhao, W	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Renewable Energy, 2012, 37 (1), pp. 241 - 249	en_US
dc.identifier.issn	0960-1481	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22301
dc.description.abstract	In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation function (PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China. © 2011 Elsevier Ltd.	en_US
dc.relation.ispartof	Renewable Energy	en_US
dc.relation.isbasedon	10.1016/j.renene.2011.06.023	en_US
dc.subject.classification	Energy	en_US
dc.title	Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	37	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0915 Interdisciplinary 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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation function (PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China. © 2011 Elsevier Ltd.

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http://hdl.handle.net/10453/22301