Unsupervised Disaggregation for Non-intrusive Load Monitoring

A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature id...

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Bibliographic Details
Published in2012 Eleventh International Conference on Machine Learning and Applications Vol. 2; pp. 515 - 520
Main Author Pattem, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Aggregates
disaggregation
Hidden Markov models
Home appliances
Power demand
Quantization
Smoothing methods
unsupervised machine learning
Viterbi algorithm
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ISBN1467346519
9781467346511
DOI10.1109/ICMLA.2012.249

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Summary:A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature identification, and disaggregation using hidden Markov modeling (HMM), and residual analysis. The key contributions are (a) a novel 'segmented' application of the Viterbi algorithm for sequence decoding with the HMM, (b) details of establishing observation and state transition probabilities for the HMM, and (c) procedures for careful handling of low power signatures. Results show that the method is effective for magnitude-based disaggregation, and provide insights for a more complete solution.
ISBN:1467346519
9781467346511
DOI:10.1109/ICMLA.2012.249