Towards automatic classification of appliances: Tackling cross talk in EMF sensors using blind source separation techniques

Abstract

Non Intrusive Load Monitoring (NILM) is the method of obtaining information about appliance-level power consumption inside a building from voltage and/or current measurements made at a central location in the electrical system. The system that does this kind of power disaggregation typically relies on some sort of training step to help it recognize what types of changes in the observed signal correspond to what kinds of appliances. To help train NILM systems recognize appliances within the house, auxiliary sensors like Electromagnetic Field (EMF) sensors have been proposed (Rowe et al., 2010). One of the problems with using EMF sensors to automatically recognize an appliance is that of cross talk. Since a typical home constitutes of settings where there are multiple appliances at close vicinity, EMF sensors are prone to picking up unwanted signals from appliances that are not of interest. In this paper, we use blind source separation techniques like Independent Component Analysis to remedy cross talk in EMF sensors.