• DocumentCode
    2629287
  • Title

    Appliance usage prediction using a time series based classification approach

  • Author

    Basu, Kaustav ; Debusschere, Vincent ; Bacha, Seddik

  • Author_Institution
    Grenoble Electr. Eng. Lab. (G2E Lab.), St. Martin d´´Hères, France
  • fYear
    2012
  • fDate
    25-28 Oct. 2012
  • Firstpage
    1217
  • Lastpage
    1222
  • Abstract
    Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.
  • Keywords
    building management systems; data mining; decision trees; energy consumption; energy management systems; pattern classification; power engineering computing; time series; appliance usage prediction; data mining; decision tree; energy consumption data; energy management; residential homes; time 1 hour; time series based classification approach; time-series based multilabel classifier approach; Accuracy; Bayesian methods; Europe; Load modeling; Microwave measurements; Ovens; Appliance Usage Prediction; Data Mining; Energy Management in Homes; Learning Algorithm; Multi-label classifiers; Smart-Buildings; decision tree;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society
  • Conference_Location
    Montreal, QC
  • ISSN
    1553-572X
  • Print_ISBN
    978-1-4673-2419-9
  • Electronic_ISBN
    1553-572X
  • Type

    conf

  • DOI
    10.1109/IECON.2012.6388597
  • Filename
    6388597