Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

Wang, J; Song, Y; Liu, F; Hou, R

Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

Wang, J Song, Y

Liu, F

Hou, R

Permalink

Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Renewable and Sustainable Energy Reviews, 2016, 60, pp. 960-981
Issue Date:: 2016-07-01

Closed Access

	Filename	Description	Size
	1-s2.0-S1364032116001441-main.pdf	Published version	3.36 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Song, Y https://orcid.org/0000-0002-6633-2695
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.contributor.author	Hou, R
dc.date.accessioned	2022-08-07T05:41:55Z
dc.date.available	2022-08-07T05:41:55Z
dc.date.issued	2016-07-01
dc.identifier.citation	Renewable and Sustainable Energy Reviews, 2016, 60, pp. 960-981
dc.identifier.issn	1364-0321
dc.identifier.issn	1879-0690
dc.identifier.uri	http://hdl.handle.net/10453/159727
dc.description.abstract	Wind energy, which is clean, inexhaustible and free, has been used to mitigate the crisis of conventional resource depletion. However, wind power is difficult to implement on a large scale because the volatility of wind hinders the prediction of steady and accurate wind power or speed values, especially for multi-step-ahead and long horizon cases. Multi-step-ahead prediction of wind speed is challenging and can be realized by the Weather Research and Forecasting Model (WRF). However, a large error in wind speed will occur due to inaccurate predictions at the beginning of the synoptic process in WRF. Multi-step wind speed predictions using statistical and machine learning methods have rarely been studied because greater numbers of forecasting steps correspond to lower accuracy. In this study, a detailed review of wind speed forecasting is presented, including the application of wind energy, time horizons for wind speed prediction and wind speed forecasting methods. This paper presents eight strategies for realizing multi-step wind speed forecasting with machine-learning methods and compares 48 different hybrid models based on these eight strategies. The results show good consistency among the different wind farms, with COMB-DIRMO models generally having a higher prediction accuracy than the other strategies. Thus, this paper introduced three methods of combining these COMB-DIRMO models, an analysis of their performance improvements over the original models and a comparison among them. Valid experimental simulations demonstrate that ALL-DDVC, one combination of the COMB-DIRMO models, is a practical, effective and robust model for multi-step-ahead wind speed forecasting.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Renewable and Sustainable Energy Reviews
dc.relation.isbasedon	10.1016/j.rser.2016.01.114
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering
dc.subject.classification	Energy
dc.title	Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-07T05:41:53Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Wind energy, which is clean, inexhaustible and free, has been used to mitigate the crisis of conventional resource depletion. However, wind power is difficult to implement on a large scale because the volatility of wind hinders the prediction of steady and accurate wind power or speed values, especially for multi-step-ahead and long horizon cases. Multi-step-ahead prediction of wind speed is challenging and can be realized by the Weather Research and Forecasting Model (WRF). However, a large error in wind speed will occur due to inaccurate predictions at the beginning of the synoptic process in WRF. Multi-step wind speed predictions using statistical and machine learning methods have rarely been studied because greater numbers of forecasting steps correspond to lower accuracy. In this study, a detailed review of wind speed forecasting is presented, including the application of wind energy, time horizons for wind speed prediction and wind speed forecasting methods. This paper presents eight strategies for realizing multi-step wind speed forecasting with machine-learning methods and compares 48 different hybrid models based on these eight strategies. The results show good consistency among the different wind farms, with COMB-DIRMO models generally having a higher prediction accuracy than the other strategies. Thus, this paper introduced three methods of combining these COMB-DIRMO models, an analysis of their performance improvements over the original models and a comparison among them. Valid experimental simulations demonstrate that ALL-DDVC, one combination of the COMB-DIRMO models, is a practical, effective and robust model for multi-step-ahead wind speed forecasting.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/159727