Appliance State Estimation based on Particle Filtering

Authors:
Dominik Egarter;Venkata Pathuri Bhuvana;Wilfried Elmenreich
Affiliations:
Institute for Networked and Embedded Systems, University of Klagenfurt, Klagenfurt, Austria;Institute for Networked and Embedded Systems, University of Klagenfurt, Klagenfurt, Austria;Institute for Networked and Embedded Systems, University of Klagenfurt, Klagenfurt, Austria
Venue:
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Year:
2013

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Abstract

Non-Intrusive Load Monitoring is a single-point metering approach to identify and to monitor household appliances according their appliance power characteristics. In this paper, we propose an unsupervised classification approach for appliance state estimation of on/off-appliances modeled by a Hidden Markov Model (HMM). To estimate the states of appliances, we use the sequential Monte Carlo or particle filtering (PF) method. The proposed algorithm is tested with MATLAB simulations and is evaluated according to correctly or incorrectly detected on/off events.