Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

Zoha, A., Gluhak, A., Nati, M. and Imran, M. A. (2013) Low-Power Appliance Monitoring Using Factorial Hidden Markov Models. In: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 02-05 Apr 2013, pp. 527-532. ISBN 9781467355018 (doi:10.1109/ISSNIP.2013.6529845)

136755.pdf - Accepted Version



To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states.

Item Type:Conference Proceedings
Additional Information:We acknowledge the support from the REDUCE project grant (no:EP/I000232/1) under the Digital Economy Programme run by Research Councils UK - a cross council initiative led by EPSRC and contributed to by AHRC, ESRC, and MRC.
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: Zoha, A., Gluhak, A., Nati, M., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering
Published Online:13 June 2013
Copyright Holders:Copyright © 2013 IEEE
First Published:First published in 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing: 527-532
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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