Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation

Mingjun Zhong* (Corresponding Author), Nigel Goddard, Charles Sutton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

Blind source separation problems are difficult because they are inherently unidentifiable, yet the entire goal is to identify meaningful sources. We introduce a way of incorporating domain knowledge into this problem, called signal aggregate constraints (SACs). SACs encourage the total signal for each of the unknown sources to be close to a specified value. This is based on the observation that the total signal often varies widely across the unknown sources, and we often have a good idea of what total values to expect. We incorporate SACs into an additive factorial hidden Markov model (AFHMM) to formulate the energy disaggregation problems where only one mixture signal is assumed to be observed. A convex quadratic program for approximate inference is employed for recovering those source signals. On a real-world energy disaggregation data set, we show that the use of SACs dramatically improves the original AFHMM, and significantly improves over a recent state-of-the-art approach.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 27 (NIPS 2014)
EditorsZ Ghahramani, M Welling, C Cortes, N D Lawrence, K Q Weinberger
Place of PublicationPalais des Congrès de Montréal, Montréal, CANADA
PublisherCurran Associates, Inc.
Pages3590-3598
Number of pages9
Volume4
Publication statusPublished - 1 Jan 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014
CountryCanada
CityMontreal
Period8/12/1413/12/14

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