Unsupervised disaggregation of low frequency power measurements
Kim, Hyung Sul
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https://hdl.handle.net/2142/30909
Description
Title
Unsupervised disaggregation of low frequency power measurements
Author(s)
Kim, Hyung Sul
Issue Date
2012-05-22T00:14:34Z
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
energy
disaggregation
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.
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