Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21341

Full metadata record

DC Field	Value	Language
dc.contributor.author	Luo, XJ	-
dc.contributor.author	Oyedele, LO	-
dc.contributor.author	Ajayi, AO	-
dc.contributor.author	Akinade, OO	-
dc.contributor.author	Owolabi, HA	-
dc.contributor.author	Ahmed, A	-
dc.date.accessioned	2020-08-04T13:44:08Z	-
dc.date.available	2020-10-01	-
dc.date.available	2020-08-04T13:44:08Z	-
dc.date.issued	2020-06-29	-
dc.identifier.citation	Renewable and Sustainable Energy Reviews, 2020, 131	en_US
dc.identifier.issn	1364-0321	-
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/21341	-
dc.description.sponsorship	The Department for Business, Energy & Industrial Strategy	en_US
dc.language.iso	en	en_US
dc.publisher	Elsevier	en_US
dc.subject	Energy consumption prediction	en_US
dc.subject	Feature extraction	en_US
dc.subject	Clustering	en_US
dc.subject	Adaptive	en_US
dc.subject	Deep neural network	en_US
dc.subject	Genetic algorithm	en_US
dc.title	Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings	en_US
dc.type	Article	en_US
dc.identifier.doi	http://dx.doi.org/10.1016/j.rser.2020.109980	-
dc.relation.isPartOf	Renewable and Sustainable Energy Reviews	-
pubs.publication-status	Published	-
pubs.volume	131	-
dc.identifier.eissn	1879-0690	-
Appears in Collections:	Dept of Mechanical and Aerospace Engineering Embargoed Research Papers

Files in This Item:

File	Description	Size	Format
FullText.pdf		4.67 MB	Adobe PDF	View/Open