creators_name: Parson, Oliver
creators_name: Ghosh, Siddhartha
creators_name: Weal, Mark
creators_name: Rogers, Alex
type: conference_item
datestamp: 2012-05-10 09:16:55
lastmod: 2012-10-03 10:59:44
metadata_visibility: show
title: Non-intrusive load monitoring using prior models of general appliance types
ispublished: pub
subjects: EM
subjects: ML
subjects: app
subjects: at
full_text_status: public
pres_type: paper
abstract: 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
date_type: published
event_title: Twenty-Sixth Conference on Artificial Intelligence (AAAI-12)
event_location: Toronto, Canada
event_dates: 22-26 July 2012
event_type: conference
refereed: TRUE
citation:   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.     
document_url: http://www.orchid.ac.uk/eprints/49/1/nialm.pdf