SDP Relaxation with Randomized Rounding for Energy Disaggregation
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 paper proposed method to find better approximate solution for energy disaggregation or non-intrusive load monitoring (NILM) problem based on the previous work done by Kolter & Jaakkola (2012) who used an additive factorial HMM to model the energy consumption. The authors combined a SDP relaxation and randomized rounding (Park & Boyd, 2015) and applied ADMM (Boyd, 2010). The proposed method achieved superior performance to the existing method.
It is meaningful that the proposed method perform outstandingly well on simulation results in aspects of accuracy. However, this paper lacks academic novelty and originality in that the proposed method solely combined existing works. The authors argue that proposed algorithm is scalable and computationally efficient, but there is no explanation about computational complexity on method or experimental results. It is necessary to show effectiveness in aspects of computational complexity. Alternatively, comparison with existing methods on execution time or experiments using large-scale data could be provided. This paper is well written overall and states essential information. The author should modify some mistakes in expression including the title of section 6 and its subsection, line 75 and line 223. It would be better to represent important figures or tables on the manuscript not on the supplementary material in spite of the limited space. In addition, conclusion part is too short.