• 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