title: Non-intrusive load monitoring using prior models of general appliance types
creator: Parson, Oliver
creator: Ghosh, Siddhartha
creator: Weal, Mark
creator: Rogers, Alex
subject: Energy Management
subject: Machine Learning
subject: Applications
subject: Agile Teaming
description: Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated 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 complete 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 instances 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 approach 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 demonstrate the potential for personalised energy saving feedback.
date: 2012-07
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
identifier: http://www.orchid.ac.uk/eprints/49/1/nialm.pdf
identifier:   Parson, Oliver and Ghosh, Siddhartha and Weal, Mark and Rogers, Alex  (2012) Non-intrusive load monitoring using prior models of general appliance types.  In: Twenty-Sixth Conference on Artificial Intelligence (AAAI-12), 22-26 July 2012, Toronto, Canada.     
relation: http://www.orchid.ac.uk/eprints/49/