SDP Relaxation with Randomized Rounding for Energy Disaggregation
This addresses the problem of accurately estimating individual appliance energy consumption from total household signals for energy monitoring applications, representing an incremental improvement over existing methods.
The authors tackled the problem of energy disaggregation for home appliance monitoring by developing a method that combines convex semidefinite relaxations with randomized rounding and a scalable ADMM approach. Their method demonstrated superiority over the state-of-the-art factorial HMM approach in both synthetic and real-world datasets.
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.