Nonintrusive load monitoring using prior models of general appliance types

Non-intrusive appliance load monitoring is the process of dis-aggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an ap-proach by which individual appliances can be iteratively sep-arated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume com-plete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance in-stances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our ap-proach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demon-strate the potential for personalised energy saving feedback.