Unsupervised disaggregation of low frequency power measurements

Hyungsul Kim, Manish Marwah, Martin Arlitt, Geoff Lyon, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
Pages747-758
Number of pages12
StatePublished - Dec 1 2011
Event11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States
Duration: Apr 28 2011Apr 30 2011

Publication series

NameProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Other

Other11th SIAM International Conference on Data Mining, SDM 2011
CountryUnited States
CityMesa, AZ
Period4/28/114/30/11

ASJC Scopus subject areas

  • Software

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