  Energy disaggregation is to discover the energy consumption of individual
appliances from their aggregated energy values. To solve the problem, most
existing approaches rely on either appliances' signatures or their state
transition patterns, both hard to obtain in practice. Aiming at developing a
simple, universal model that works without depending on sophisticated machine
learning techniques or auxiliary equipments, we make use of easily accessible
knowledge of appliances and the sparsity of the switching events to design a
Sparse Switching Event Recovering (SSER) method. By minimizing the total
variation (TV) of the (sparse) event matrix, SSER can effectively recover the
individual energy consumption values from the aggregated ones. To speed up the
process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve
the problem in active epochs of appliance activities in parallel. Using
real-world trace data, we compare the performance of our method with that of
the state-of-the-art solutions, including Least Square Estimation (LSE) and
iterative Hidden Markov Model (HMM). The results show that our approach has an
overall higher detection accuracy and a smaller overhead.
