Deep learning application to non-intrusive load monitoring

dc.contributor.author	Linh, N. V.
dc.contributor.author	Arboleya Arboleya, Pablo
dc.date.accessioned	2020-01-23T08:17:41Z
dc.date.available	2020-01-23T08:17:41Z
dc.date.issued	2019
dc.identifier.isbn	9781538647226
dc.identifier.uri	http://hdl.handle.net/10651/53804
dc.description	2019 IEEE Milan PowerTech (Milán. 2019)
dc.description.sponsorship	This work was partially supported by the Spanish Ministry of Economy and Competitivity under Grant MINECO-17-ENE2016-77919-R (CONCIALIATOR Energy conversion technologies in resilient hybrid AC/DC distribution networks).
dc.format.extent	p. 8810435-
dc.language.iso	eng
dc.relation.ispartof	2019 IEEE Milan PowerTech, PowerTech 2019
dc.rights	© 2019 IEEE
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072328730&doi=10.1109%2fPTC.2019.8810435&partnerID=40&md5=438b90397910becd04259d4866bae0e5
dc.title	Deep learning application to non-intrusive load monitoring
dc.type	conference output	spa
dc.identifier.doi	10.1109/PTC.2019.8810435
dc.relation.projectID	MINECO-17-ENE2016-77919-R
dc.relation.projectID	FC-GRUPIN-IDI/2018/000241
dc.relation.publisherversion	http://dx.doi.org/10.1109/PTC.2019.8810435
dc.rights.accessRights	open access
dc.type.hasVersion	AM