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.