Mostrar el registro sencillo del ítem

Latent variable analysis in hospital electric power demand using non-negative matrix factorization

dc.contributor.authorGarcía Pérez, Diego 
dc.contributor.authorDíaz Blanco, Ignacio 
dc.contributor.authorPérez García, Daniel 
dc.contributor.authorCuadrado Vega, Abel Alberto 
dc.contributor.authorDomínguez González, Manuel
dc.date.accessioned2017-09-28T10:14:58Z
dc.date.available2017-09-28T10:14:58Z
dc.date.issued2017
dc.identifier.citationESANN 2017. European Symposium on Artificial Neural Networks, p. 507-512 (2017)
dc.identifier.isbn978-287587039-1
dc.identifier.urihttp://hdl.handle.net/10651/43805
dc.descriptionESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 26-28 April 2017spa
dc.description.abstractEnergy disaggregation techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods allow to decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, nonnegative and sum up the original measured quantity. Such components reveal hidden temporal patterns and events along this period, related to scheduling events and/or demand patterns from subsystems in the network, that are very useful within an energy efficiency context. In this paper we use this approach on demand data from a hospital during a oneyear period, using a calendar visualization of the components, revealing relevant facts about the energy expenditurespa
dc.description.sponsorshipFinancial support from the Spanish Ministry of Economy (MINECO)and FEDER funds from the EU under grant DPI2015-69891-C2-1/2-Rspa
dc.format.extentp. 507-512spa
dc.language.isoengspa
dc.publisheri6doc.com publicationspa
dc.relation.ispartofESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learningspa
dc.rights© i6doc.com publication
dc.titleLatent variable analysis in hospital electric power demand using non-negative matrix factorizationspa
dc.typeconference outputspa
dc.relation.projectIDDPI2015-69891-C2-1/2-Rspa
dc.relation.publisherversionhttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2017-60.pdfspa
dc.rights.accessRightsopen accessspa


Ficheros en el ítem

untranslated

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem