SDP Relaxation with Randomized Rounding for Energy Disaggregation

TL;DR

This paper introduces a scalable energy disaggregation method using SDP relaxation and randomized rounding, outperforming existing factorial HMM-based approaches in estimating appliance energy use from aggregate signals.

Contribution

It presents a novel convex relaxation and randomized rounding approach tailored for energy disaggregation, improving accuracy and scalability over prior methods.

Findings

Outperforms state-of-the-art factorial HMM methods in accuracy.

Demonstrates effectiveness on synthetic and real-world datasets.

Offers a scalable ADMM-based solution for large-scale problems.

Abstract

We develop a scalable, computationally efficient method for the task of energy disaggregation for home appliance monitoring. In this problem the goal is to estimate the energy consumption of each appliance over time based on the total energy-consumption signal of a household. The current state of the art is to model the problem as inference in factorial HMMs, and use quadratic programming to find an approximate solution to the resulting quadratic integer program. Here we take a more principled approach, better suited to integer programming problems, and find an approximate optimum by combining convex semidefinite relaxations randomized rounding, as well as a scalable ADMM method that exploits the special structure of the resulting semidefinite program. Simulation results both in synthetic and real-world datasets demonstrate the superiority of our method.