Work

Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation

Year: 2014

Type: article

Abstract: Blind source separation problems are difficult because they are inherently uniden-tifiable, 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 im-proves over a recent state-of-the-art approach. 1

Source: Lincoln Repository (University of Lincoln)

Authors: Mingjun Zhong, Nigel Goddard, Charles Sutton

Institution: University of Edinburgh

Language: English

Topic: Blind Source Separation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Award: IDEAL: Intelligent Domestic Energy Advice Loop

OpenAlex ID: W2115033874