Timezone: »

SDP Relaxation with Randomized Rounding for Energy Disaggregation
Kiarash Shaloudegi · András György · Csaba Szepesvari · Wilsun Xu

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #194

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 based on the total energy-consumption signal of a household. The current state of the art models the problem as inference in factorial HMMs, and finds an approximate solution to the resulting quadratic integer program via quadratic programming. Here we take a more principled approach, better suited to integer programming problems, and find an approximate optimum by combining convex semidefinite relaxations with randomized rounding, as well as with a scalable ADMM method that exploits the special structure of the resulting semidefinite program. Simulation results demonstrate the superiority of our methods both in synthetic and real-world datasets.

Author Information

Kiarash Shaloudegi (Huawei Noah's Ark)
András György (Google DeepMind)
Csaba Szepesvari (U. Alberta)
Wilsun Xu (University of Alberta)

More from the Same Authors