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. In, Neural Information Processing Systems workshop on Machine Learning for Sustainability, Sierra Nevada, Spain,


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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: 17 December 2011
Keywords: Non-intrusive appliance load monitoring, energy disaggregation
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 272990
Accepted Date and Publication Date:
17 December 2011Published
Date Deposited: 09 Nov 2011 23:50
Last Modified: 31 Mar 2016 14:22
Further Information:Google Scholar

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