Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21341
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dc.contributor.authorLuo, XJ-
dc.contributor.authorOyedele, LO-
dc.contributor.authorAjayi, AO-
dc.contributor.authorAkinade, OO-
dc.contributor.authorOwolabi, HA-
dc.contributor.authorAhmed, A-
dc.date.accessioned2020-08-04T13:44:08Z-
dc.date.available2020-10-01-
dc.date.available2020-08-04T13:44:08Z-
dc.date.issued2020-06-29-
dc.identifier.citationRenewable and Sustainable Energy Reviews, 2020, 131en_US
dc.identifier.issn1364-0321-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/21341-
dc.description.sponsorshipThe Department for Business, Energy & Industrial Strategyen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEnergy consumption predictionen_US
dc.subjectFeature extractionen_US
dc.subjectClusteringen_US
dc.subjectAdaptiveen_US
dc.subjectDeep neural networken_US
dc.subjectGenetic algorithmen_US
dc.titleFeature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildingsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.rser.2020.109980-
dc.relation.isPartOfRenewable and Sustainable Energy Reviews-
pubs.publication-statusPublished-
pubs.volume131-
dc.identifier.eissn1879-0690-
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Embargoed Research Papers

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