Unsupervised disaggregation of low frequency power measurements

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2012

Author dc:creator

• Kim, Hyung Sul

Contributors dc:contributor

• Han, Jiawei

• energy
• disaggregation

Statement dc:rights

• Copyright 2012 Hyung Sul Kim

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/30909

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/30909