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

Abstract

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