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Non-intrusive load monitoring using prior models of general appliance types

Non-intrusive load monitoring using prior models of general appliance types
Non-intrusive load monitoring using prior models of general appliance types
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.
356-362
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Parson, Oliver, Ghosh, Siddhartha, Weal, Mark and Rogers, Alex (2012) Non-intrusive load monitoring using prior models of general appliance types At Proceedings of theTwenty-Sixth Conference on Artificial Intelligence (AAAI-12), Canada. 22 - 26 Jul 2012. , pp. 356-362.

Record type: Conference or Workshop Item (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.

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More information

Published date: 24 July 2012
Venue - Dates: Proceedings of theTwenty-Sixth Conference on Artificial Intelligence (AAAI-12), Canada, 2012-07-22 - 2012-07-26
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 336812
URI: http://eprints.soton.ac.uk/id/eprint/336812
PURE UUID: 23c05094-8454-44cb-add2-9b2432ae1ab4
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

Catalogue record

Date deposited: 06 Apr 2012 13:18
Last modified: 03 Oct 2017 16:40

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Contributors

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

University divisions

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