A novel DNN-HMM-based approach for extracting single loads from aggregate power signals

This paper presents a new supervised approach to extract the power trace of individual loads from single channel aggregate power signals in non-intrusive load monitoring (NILM) systems. Recent approaches to this source separation problem are based on factorial hidden markov models (FHMM). Drawbacks are the needed knowledge of HMM models for all loads, what is infeasible for large buildings, and the large combinatorial complexity. Our approach trains HMM with two emission probabilities, one for the single load to be extracted and the other for the aggregate power signal. A Gaussian distribution is used to model observations of the single load whereas observations of the aggregate signal are modeled with a Deep Neural Network (DNN). By doing so, a single load can be extracted from the aggregate power signal without knowledge of the remaining loads. The performance of the algorithm is evaluated on the Reference Energy Disag-gregation (REDD) dataset.