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

SDP Relaxation with Randomized Rounding for Energy Disaggregation