• DocumentCode
    3592214
  • Title

    Electricity load classification using K-means clustering algorithm

  • Author

    Nuchprayoon, Somboon

  • Author_Institution
    Dept. of Electr. Eng., Chiang Mai Univ., Chiang Mai, Thailand
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    K-means clustering method is applied to classify electricity load data into five groups. The load groups are super-peak, peak, cycling, intermediate, and base. On the other hand, when only three groups are needed, the peak load is combined with the cycling load and the intermediate load is combined with the base load. The classification is performed both on annual basis and seasonal basis and shown by using load duration curves. The attributes of load group are load level and duration. The proposed method has been implemented by using statistical analysis software SPSS and tested with the hourly generation data of Thailand during 2009-2011.
  • Keywords
    demand side management; load forecasting; power engineering computing; statistical analysis; AD 2009 to 2011; K-means clustering algorithm; Thailand; cycling load; electricity load classification; generation data; intermediate load; load duration curves; load groups; peak load; statistical analysis software SPSS; electricity load; k-means clustering; load duration curve; load group;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Engineering and Technology (BICET 2014), 5th Brunei International Conference on
  • Print_ISBN
    978-1-84919-991-9
  • Type

    conf

  • DOI
    10.1049/cp.2014.1061
  • Filename
    7120239