dc.contributor.author | Werthen Brabants, Lorin | |
dc.contributor.author | Dhaene, Tom | |
dc.contributor.author | Deschrijver, Dirk | |
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.issued | 2022 | |
dc.identifier.issn | 0378-7788 | |
dc.identifier.other | WOS:000834532400003 | |
dc.identifier.uri | https://imec-publications.be/handle/20.500.12860/40294.3 | |
dc.source | WOS | |
dc.title | Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning | |
dc.type | Journal article | |
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.embargo | 9999-12-31 | |
dc.identifier.doi | 10.1016/j.enbuild.2022.112282 | |
dc.source.numberofpages | 5 | |
dc.source.peerreview | yes | |
dc.source.beginpage | 112282 | |
dc.source.endpage | na | |
dc.source.journal | ENERGY AND BUILDINGS | |
dc.source.issue | / | |
dc.source.volume | 270 | |
imec.availability | Published - open access | |
dc.description.wosFundingText | This research received funding from the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme. | |