Non-intrusive appliance load monitoring using low-resolution smart meter data

We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reprod...

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Bibliographic Details
Published in2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) pp. 535 - 540
Main Authors Jing Liao, Elafoudi, Georgia, Stankovic, Lina, Stankovic, Vladimir
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Accuracy
Feature extraction
Hidden Markov models
Home appliances
Image edge detection
Libraries
Training
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DOI10.1109/SmartGridComm.2014.7007702

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Summary:We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using three US and three UK households, show that both proposed methods outperform the HMM-based approach and are capable of disaggregating a range of domestic loads even when the training period is very short.
DOI:10.1109/SmartGridComm.2014.7007702