EMF Signature for Appliance Classification

Various intrusive and nonintrusive appliance load monitoring and classification systems have been studied; however, most of them designed so far provide group-level energy usage feedback. We present the first phase of a system with the potential to attribute energy-related events to an individual occupant of a space and provide occupant-specific energy usage feedback in an uninstrumented space (e.g., home or office). This initial phase focuses on collecting the electromagnetic field (EMF) radiated by several common appliances to determine a unique signature for each appliance. It also implements a machine learning algorithm to classify appliances from an incoming EMF data file. The proposed approach has been prototyped with hardware realization. The results obtained on tested appliances indicate the EMF sensor's ability and potential to develop a system for providing occupant-specific energy feedback.