Abstract
Conference Title: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Conference Start Date: 2018, April 15 Conference End Date: 2018, April 20 Conference Location: Calgary, AB, Canada Energy disaggregation is the task of decomposing the aggregated power consumption readings of a household into its constituent parts. In this paper, we propose a supervised, non-parametric framework for energy disaggregation. We demonstrate that the problem is equivalent to maximizing a set-function subject to combinatorial constraints, which is NP-hard in its general form. A simple polynomial-time successive approximation algorithm which exploits submodularity per set-block to iteratively maximize a sequence of global lower bounds of the objective function is proposed for obtaining approximate solutions. Experiments on real data indicate the superior disaggregation performance and scalability of our approach over a state-of-the-art parametric Factorial Hidden Markov Model based framework employing convex relaxation.