SDP Relaxation with Randomized Rounding for Energy Disaggregation

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• Kiarash Shaloudegi (PhD student at ISN)
• Tuesday 29 November 2016, 11:00-12:00
• Room 611 (Gabor Seminar Room), EEE Building.

If you have a question about this talk, please contact Wiesia R Hsissen.

Abstract: 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.

This talk is part of the Featured talks series.

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• Andrea Picciau's list
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• COMMSP Seminar
• Featured talks
• Room 611 (Gabor Seminar Room), EEE Building
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