• DocumentCode
    2955073
  • Title

    Energy disaggregation using ensemble of classifiers

  • Author

    Shahriar, M.S. ; Rahman, Aminur

  • Author_Institution
    ICT Centre, Intell. Sensing & Syst. Lab. (ISSL), CSIRO, Hobart, TAS, Australia
  • fYear
    2013
  • fDate
    17-19 April 2013
  • Firstpage
    161
  • Lastpage
    164
  • Abstract
    We study an approach towards energy disaggregation using ensemble of classifiers, a supervised machine learning method. Specifically we identify different appliance loads from the aggregated power usage data. Experimental results on a public data sets show the accuracy of ensemble of classifiers using diverse features in identifying appliance loads.
  • Keywords
    learning (artificial intelligence); pattern classification; aggregated power usage data; classifiers; energy disaggregation; machine learning; Accuracy; Bagging; Context; Feature extraction; Home appliances; Sensors; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON Spring Conference, 2013 IEEE
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-1-4673-6347-1
  • Type

    conf

  • DOI
    10.1109/TENCONSpring.2013.6584433
  • Filename
    6584433
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2955073