Disaggregating Transform Learning for Non-Intrusive Load Monitoring

This paper addresses the problem of energy disaggregation/non-intrusive load monitoring. It introduces a new method based on the transform learning formulation. Several recent techniques, such as discriminative sparse coding, powerlet disaggregation, and deep sparse coding, are based on the synthesis dictionary learning/sparse coding approach; ours is based on its analysis equivalent. The theoretical advantage of analysis dictionary compared to its synthesis counterpart is that the former can learn from fewer training samples-this has implications in reducing the cost of energy disaggregation. Experiments have been carried out on two benchmark data sets-REDD and Dataport (Pecan Street). Comparison has been done with factorial HMM, discriminative sparse coding, powerlet disaggregation, and deep sparse coding. In the low training data regime, our method always excels over the others.