Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning

dc.contributor.author	Werthen Brabants, Lorin
dc.contributor.author	Dhaene, Tom
dc.contributor.author	Deschrijver, Dirk
dc.contributor.imecauthor	Werthen Brabants, Lorin
dc.contributor.imecauthor	Dhaene, Tom
dc.contributor.imecauthor	Deschrijver, Dirk
dc.contributor.orcidimec	Dhaene, Tom::0000-0003-2899-4636
dc.contributor.orcidimec	Deschrijver, Dirk::0000-0001-6600-1792
dc.contributor.orcidimec	Werthen Brabants, Lorin::0000-0001-9976-1886
dc.date.accessioned	2022-09-27T10:11:48Z
dc.date.available	2022-08-24T02:35:04Z
dc.date.available	2022-09-05T10:05:20Z
dc.date.available	2022-09-27T10:11:48Z
dc.date.embargo	9999-12-31
dc.date.issued	2022
dc.description.wosFundingText	This research received funding from the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme.
dc.identifier.doi	10.1016/j.enbuild.2022.112282
dc.identifier.issn	0378-7788
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/40294
dc.publisher	ELSEVIER SCIENCE SA
dc.source.beginpage	112282
dc.source.endpage	na
dc.source.issue	/
dc.source.journal	ENERGY AND BUILDINGS
dc.source.numberofpages	5
dc.source.volume	270
dc.title	Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning
dc.type	Journal article
dspace.entity.type	Publication
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