  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.
