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Repositorio de la Universidad de Oviedo > Producción Bibliográfica de UniOvi: RECOPILA > Capítulos de libros >

Use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10651/43805

Título : Latent variable analysis in hospital electric power demand using non-negative matrix factorization
Autor(es) y otros: García, Diego
Díaz Blanco, Ignacio
Pérez, Daniel
Cuadrado Vega, Abel Alberto
Domínguez, Manuel
Fecha de publicación : 2017
Editorial : i6doc.com publication
Versión del editor: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2017-60.pdf
Citación : ESANN 2017. European Symposium on Artificial Neural Networks, p. 507-512 (2017)
Descripción física: p. 507-512
Resumen : Energy 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 expenditure
Descripción : ESANN 2017 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 26-28 April 2017
URI : http://hdl.handle.net/10651/43805
ISBN : 978-287587039-1
Aparece en las colecciones: Capítulos de libros
Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas
Investigaciones y Documentos OpenAIRE

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