Multi-Label Auto-Encoder based Electrical Load Disaggregation

Load Disaggregation has gained much popularity in the recent times, owing to the advantages it brings to energy utility companies. Many modeling techniques ranging from Dictionary Learning to HMM-based techniques to Neural Network based modeling have been proposed in the literature to solve this problem. However, scalability and computational lightness, have been two main areas of concern associated with the problem modeling. In this work, the authors propose to use Multi-Label Auto-Encoder architecture to solve this problem. The proposed architecture incurs minimum instrumentation cost, which makes it truly non-intrusive. The use of superposed appliance class labels of interest in the discriminative penalizing term of the architecture, ensures that disaggregation is achieved without the need to train a separate model for each appliance class of interest.