• DocumentCode
    1336096
  • Title

    Load Signature Study—Part II: Disaggregation Framework, Simulation, and Applications

  • Author

    Liang, Jian ; Ng, Simon K.K. ; Kendall, Gail ; Cheng, John W.M.

  • Author_Institution
    CLP Res. Inst. Ltd., Hong Kong, China
  • Volume
    25
  • Issue
    2
  • fYear
    2010
  • fDate
    4/1/2010 12:00:00 AM
  • Firstpage
    561
  • Lastpage
    569
  • Abstract
    Load signatures embedded in common electricity consumption patterns, in fact, could render much information pertaining to the nature of the appliances and their usage patterns. Based on the proposed disaggregation framework, we use three advanced disaggregation algorithms, called committee decision mechanisms (CDMs), to perform load disaggregation at the metering level. Three random switching simulators are also developed to investigate the performance of different CDMs under a variety of scenarios. Through Monte Carlo simulations, we demonstrate that all CDMs outperform any single-feature, single-algorithm-based disaggregation methods. With sensitivity analysis, we also show that the CDMs are less sensitive to any load dynamics and noise. We finally demonstrate some applications of this technology in terms of appliance usage tacking and estimated energy consumption of each appliance.
  • Keywords
    Monte Carlo methods; domestic appliances; load management; power consumption; sensitivity analysis; Monte Carlo simulations; appliance usage tacking; committee decision mechanisms; electricity consumption patterns; energy consumption; load disaggregation algorithms; load signatures; random switching simulators; sensitivity analysis; Committee decision mechanism; Monte Carlo methods; electric-load intelligence; load disaggregation; load signature; smart metering;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/TPWRD.2009.2033800
  • Filename
    5337970