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Title:Unsupervised disaggregation of low frequency power measurements
Author(s):Kim, Hyung Sul
Advisor(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Abstract:Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide per-appliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.
Issue Date:2012-05-22
Rights Information:Copyright 2012 Hyung Sul Kim
Date Available in IDEALS:2012-05-22
Date Deposited:2012-05

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