• DocumentCode
    3104273
  • Title

    Usage analysis for smart meter management

  • Author

    Li, Hongfei ; Fang, Dongping ; Mahatma, Shilpa ; Hampapur, Arun

  • Author_Institution
    IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA
  • fYear
    2011
  • fDate
    2-3 Nov. 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Smart meters gather utility usage data, such as water, electricity and gas readings, by remote reporting. The flood of usage data obtained from each meter in realtime or near real-time enable data analytics and optimization tool to support smart meter management. Predictive usage analytics can provide significant benefits to both the utilities and customers. We propose statistical approaches for meter anomaly detection, usage demand forecasting and association analysis for utility companies. These analyses provide efficient ways to detect malfunctioning meters, optimize water supply in the future and understand the association factors that drive meter failures and water demand. We illustrate our methodology using the automated meter reading (AMR) database from a water utility customer.
  • Keywords
    demand forecasting; load forecasting; optimisation; power system management; real-time systems; automated meter reading database; data analytics; drive meter failures; malfunctioning meters; meter anomaly detection; near real-time; optimization tool; predictive usage analytics; smart meter management; usage analysis; usage demand forecasting; utility companies; utility usage data; water demand; water utility customer; Analytical models; Demand forecasting; Meteorology; Meter reading; Predictive models; Time series analysis; Water resources; Anomaly Detection; Association Analysis; Automated Meter Reading (AMR); Demand Forecasting; Smart Meter Management; Usage Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Technologies for a Smarter World (CEWIT), 2011 8th International Conference & Expo on
  • Conference_Location
    New York, NY
  • Print_ISBN
    978-1-4577-1592-1
  • Electronic_ISBN
    978-1-4577-1590-7
  • Type

    conf

  • DOI
    10.1109/CEWIT.2011.6135871
  • Filename
    6135871