Hidden Markov Models for Nonintrusive Appliance Load Monitoring
A method of device modeling for nonintrusive appliance load monitoring (NIALM) is presented. The proposed method uses hidden Markov models to describe device behavior. Unlike previous methods of device modeling, observations are associated with instantaneous power measurements as opposed to step changes in power use or on-off transients. The training procedure for individual devices is discussed. Accuracies of seven different device models are assessed using k-fold cross validation. In this assessment, the correlations between sequences of known state transitions and calculated Viterbi sequences representing predicted transitions are determined. This process is repeated for power use profiles collected at different sampling rates. Individual devices' Viterbi sequences are shown to be able to accurately approximate the actual device power use.
J. A. Mueller et al., "Hidden Markov Models for Nonintrusive Appliance Load Monitoring," Proceedings of the North American Power Symposium (2014, Pullman, WA), Institute of Electrical and Electronics Engineers (IEEE), Sep 2014.
The definitive version is available at http://dx.doi.org/10.1109/NAPS.2014.6965464
North American Power Symposium (2014: Sep. 7-9, Pullman, WA)
Electrical and Computer Engineering
Keywords and Phrases
Electric Load Management; Cross Validation; Disaggregation; Individual Devices; Instantaneous Power; Non-Intrusive Appliance Load Monitoring; Nonintrusive Load Monitoring; State Transitions; Training Procedures; Hidden Markov Models; Appliance Modeling
International Standard Book Number (ISBN)
Article - Conference proceedings
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