DocumentCode :
1790158
Title :
Resident-presence/absence estimation by unsupervised threshold learning for home energy consumption and its application to resident profiling
Author :
Umeno, Shinya ; Shingaki, Ryusei
Author_Institution :
Corp. R&D Center, Toshiba Corp., Kawasaki, Japan
fYear :
2014
fDate :
22-25 June 2014
Firstpage :
1
Lastpage :
2
Abstract :
We present a new method to estimate resident presence/absence status of a home using only home total power consumption data. The method is unsupervised in that the threshold computation for estimation is conducted without training data of presence/absence. From the estimation records collected for a certain period, we also automatically analyze a lifestyle profile of the residents living in the home.
Keywords :
building management systems; energy management systems; home automation; unsupervised learning; estimation record collection; home energy consumption; lifestyle profile; resident profiling process; resident-absence estimation; resident-presence estimation; total power consumption data; unsupervised threshold learning; Accuracy; Data mining; Density functional theory; Estimation; Histograms; Monitoring; Power demand; resident behavior estimation; user profiling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on
Conference_Location :
JeJu Island
Type :
conf
DOI :
10.1109/ISCE.2014.6884296
Filename :
6884296
Link To Document :
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