Open AccessProceedings Article
Unsupervised disaggregation of low frequency power measurements
Hyungsul Kim,Manish Marwah,Martin Arlitt,Geoff Lyon,Jiawei Han +4 more
- pp 747-758
TLDR
This work investigates the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes and indicates 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 unsuper supervision methods.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 perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.read more
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