Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types

By Oliver Parson, Siddhartha Ghosh, Mark James Weal, Alex Rogers. AAAI Conference on Artificial Intelligence, 2012

382 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9f204fa895ae103b432b555a801cd6a3a452d759

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Citations in the corpus

The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “[27], we downsampled the data to a granularity of 1/60 Hz.”
AMPds: A public dataset for load disaggregation and eco-feedback research (2013, background): “Gaussian) [5], [6], [8] and generally ignores the idea of discrete probability.”
NILMTK: an open source toolkit for non-intrusive load monitoring (2014, methodology): “[21] proposed a metric which captures the error in assigned energy consumed over the complete du-”