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Using Hidden Markov Models for Iterative Non-intrusive Appliance Monitoring

Parson, Oliver, Ghosh, Siddhartha, Weal, Mark and Rogers, Alex (2011) Using Hidden Markov Models for Iterative Non-intrusive Appliance Monitoring At Neural Information Processing Systems workshop on Machine Learning for Sustainability, Spain.

Record type: Conference or Workshop Item (Paper)

Abstract

Non-intrusive appliance load monitoring is the process of breaking down a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances are iteratively separated from the aggregate load. Our approach does not require training data to be collected by sub-metering individual appliances. Instead, 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 used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. We evaluate our approach using the REDD data set, and show that it can disaggregate 35% of a typical household’s total energy consumption to an accuracy of 83% by only disaggregating three of its highest energy consuming appliances.

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More information

Published date: 17 December 2011
Additional Information: Event Dates: 17 December 2011
Venue - Dates: Neural Information Processing Systems workshop on Machine Learning for Sustainability, Spain, 2011-12-17
Keywords: Non-intrusive appliance load monitoring, energy disaggregation
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 272990
URI: http://eprints.soton.ac.uk/id/eprint/272990
PURE UUID: b53da050-50fe-4c6c-b4a9-14d9c4c9ffa1
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

Catalogue record

Date deposited: 09 Nov 2011 23:50
Last modified: 18 Jul 2017 06:18

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Contributors

Author: Oliver Parson
Author: Siddhartha Ghosh
Author: Mark Weal ORCID iD
Author: Alex Rogers

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