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Energy disaggregation is to discover the energy consumption of individualappliances from their aggregated energy values. To solve the problem, mostexisting approaches rely on either appliances' signatures or their statetransition patterns, both hard to obtain in practice. Aiming at developing asimple, universal model that works without depending on sophisticated machinelearning techniques or auxiliary equipments, we make use of easily accessibleknowledge of appliances and the sparsity of the switching events to design aSparse Switching Event Recovering (SSER) method. By minimizing the totalvariation (TV) of the (sparse) event matrix, SSER can effectively recover theindividual energy consumption values from the aggregated ones. To speed up theprocess, a Parallel Local Optimization Algorithm (PLOA) is proposed to solvethe problem in active epochs of appliance activities in parallel. Usingreal-world trace data, we compare the performance of our method with that ofthe state-of-the-art solutions, including Least Square Estimation (LSE) anditerative Hidden Markov Model (HMM). The results show that our approach has anoverall higher detection accuracy and a smaller overhead.

Plug and Play! A Simple, Universal Model for Energy Disaggregation