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oai:www.ideals.illinois.edu:2142/30909 2023-07-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Han, Jiawei Kim, Hyung Sul 2012-05-22T00:14:34Z 2012-05-22T00:14:34Z 2012-05 2012-05-22T00:14:34Z 2012-05 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-03-14T20:57:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kim_Hyungsul.pdf: 393551 bytes, checksum: 5dd7c092ed754e9e5bf0a0334f1c24a4 (MD5) Made available in DSpace on 2012-05-22T00:14:34Z (GMT). No. of bitstreams: 2 Kim_Hyungsul.pdf: 393551 bytes, checksum: 5dd7c092ed754e9e5bf0a0334f1c24a4 (MD5) license.txt: 4057 bytes, checksum: d02bdd35caa3adcd2723c3fee9cb3c8c (MD5) http://hdl.handle.net/2142/30909 en Copyright 2012 Hyung Sul Kim energy disaggregation Unsupervised disaggregation of low frequency power measurements text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. PHD:Computer Science -UIUC 10KS0112PHD