An increasing interest in energy-efficiency combined with the decreasing cost of embedded networked sensors is lowering the cost of outlet-level metering. If these trends continue, new buildings in the near future will be able to install 'smart' outlets, which monitor and transmit an outlets power usage in real time, for nearly the same cost as conventional outlets. One problem with the pervasive deployment of smart outlets is that users must currently identify the specific device plugged into each meter, and then manually update the outlets meta-data in software whenever a new device is plugged into the outlet. Correct meta-data is important in both interpreting historical outlet energy data and using the data for building management. To address this problem, we propose Non-Intrusive Load Identification (NILI), which automatically identifies the device attached to a smart outlet without any human intervention. In particular, in our approach to NILI, we identify an intuitive and simple-to-compute set of features from time-series energy data and then employ well-known classifiers. Our results achieve accuracy of over 90% across 15 device types on outlet-level energy traces collected from multiple real homes.
Barker, Sean; Musthag, Mohamed; Irwin, David; and Shenoy, Prashant, "Non-intrusive load identification for smart outlets" (2015). Computer Science Faculty Publications. 2.