• DocumentCode
    41135
  • Title

    Household Energy Consumption Segmentation Using Hourly Data

  • Author

    Jungsuk Kwac ; Flora, June ; Rajagopal, Ram

  • Author_Institution
    Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA
  • Volume
    5
  • Issue
    1
  • fYear
    2014
  • fDate
    Jan. 2014
  • Firstpage
    420
  • Lastpage
    430
  • Abstract
    The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.
  • Keywords
    demand side management; power consumption; smart meters; AMI; CA smart meter data; encoding system; energy lifestyle segmentation strategies; hourly data; household electricity segmentation methodology; household energy consumption segmentation; pre-processed load shape dictionary; residential advanced metering infrastructure; Clustering algorithms; Dictionaries; Electricity; Encoding; Shape; Sociology; Statistics; Clustering; demand response; segmentation; smart meter data; variability;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2013.2278477
  • Filename
    6693793