Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy

Niu, T

Wang, J Zhang, K Du, P

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Field	Value	Language
dc.contributor.author	Niu, T https://orcid.org/0000-0002-0986-517X	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhang, K	en_US
dc.contributor.author	Du, P	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	RENEWABLE ENERGY, 2018, 118 pp. 213 - 229 (17)	en_US
dc.identifier.issn	0960-1481	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134128
dc.language	English	en_US
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en_US
dc.relation.ispartof	RENEWABLE ENERGY	en_US
dc.relation.isbasedon	10.1016/j.renene.2017.10.075	en_US
dc.subject.classification	Energy	en_US
dc.title	Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy	en_US
dc.type	Journal Article
utslib.citation.volume	118	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical 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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	118	en_US

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