An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed

Zhao, J; Guo, ZH; Su, ZY; Zhao, ZY; Xiao, X; Liu, F

An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed

Zhao, J Guo, ZH Su, ZY Zhao, ZY Xiao, X Liu, F

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Applied Energy, 2016, 162, pp. 808-826
Issue Date:: 2016-01-15

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Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Guo, ZH
dc.contributor.author	Su, ZY
dc.contributor.author	Zhao, ZY
dc.contributor.author	Xiao, X
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129
dc.date.accessioned	2022-08-13T21:30:06Z
dc.date.available	2022-08-13T21:30:06Z
dc.date.issued	2016-01-15
dc.identifier.citation	Applied Energy, 2016, 162, pp. 808-826
dc.identifier.issn	0306-2619
dc.identifier.issn	1872-9118
dc.identifier.uri	http://hdl.handle.net/10453/160099
dc.description.abstract	Accurate wind speed forecasting, which strongly influences the safe usage of wind resources, is still a critical issue and a huge challenge. At present, the single-valued deterministic NWP forecast is primarily adopted by wind farms; however, recent techniques cannot meet the actual needs of grid dispatch in many cases. This paper contributes to a new multi-step forecasting method for operational wind forecast, 96-steps of the next day, termed the CS-FS-WRF-E model, which is based on a Weather Research and Forecasting (WRF) ensemble forecast, a novel Fuzzy System, and a Cuckoo Search (CS) algorithm. First, the WRF ensemble, which considers three horizontal resolutions and four initial fields, using a 0.5° horizontal grid-spacing Global Forecast System (GFS) model output, is constructed as the basic forecasting results. Then, a novel fuzzy system, which can extract the features of these ensembles, is built under the concept of membership degrees. With the help of CS optimization, the final model is constructed using this evolutionary algorithm to adjust and correct the results obtained based on physical laws, yielding the best forecasting performance and outperforming individual ensemble members and all of the other models for comparison.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2015.10.145
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.title	An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed
dc.type	Journal Article
utslib.citation.volume	162
utslib.for	09 Engineering
utslib.for	14 Economics
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	2022-08-13T21:30:01Z
pubs.publication-status	Published
pubs.volume	162

Abstract:

Accurate wind speed forecasting, which strongly influences the safe usage of wind resources, is still a critical issue and a huge challenge. At present, the single-valued deterministic NWP forecast is primarily adopted by wind farms; however, recent techniques cannot meet the actual needs of grid dispatch in many cases. This paper contributes to a new multi-step forecasting method for operational wind forecast, 96-steps of the next day, termed the CS-FS-WRF-E model, which is based on a Weather Research and Forecasting (WRF) ensemble forecast, a novel Fuzzy System, and a Cuckoo Search (CS) algorithm. First, the WRF ensemble, which considers three horizontal resolutions and four initial fields, using a 0.5° horizontal grid-spacing Global Forecast System (GFS) model output, is constructed as the basic forecasting results. Then, a novel fuzzy system, which can extract the features of these ensembles, is built under the concept of membership degrees. With the help of CS optimization, the final model is constructed using this evolutionary algorithm to adjust and correct the results obtained based on physical laws, yielding the best forecasting performance and outperforming individual ensemble members and all of the other models for comparison.

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http://hdl.handle.net/10453/160099