• DocumentCode
    1740390
  • Title

    Next day load curve forecasting using self organizing map

  • Author

    Senjyu, Tomonobu ; Tamaki, Yoshinori ; Uezato, Katsumi

  • Author_Institution
    Ryukyus Univ., Okinawa, Japan
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    1113
  • Abstract
    In this paper, we propose a new prediction scheme using self organizing map for next day load curve forecasting. In the proposed scheme, we select several similar days corresponding to forecasted day using a Kohonen network which is a representative of self organizing map, and we forecast the next day load curve by averaging selected similar days. Therefore, we do not need complex algorithm and structure such as supervised neural network, genetic algorithm (GA) and fuzzy inference, and we can forecast next day load curve easily. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan
  • Keywords
    load forecasting; power system analysis computing; self-organising feature maps; Japan; Kohonen network; Okinawa Electric Power Company; next day load curve forecasting; prediction scheme; selected similar days averaging; self organizing map; Casting; Fuzzy neural networks; Genetic algorithms; Inference algorithms; Load forecasting; Neural networks; Neurons; Organizing; Power system control; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power System Technology, 2000. Proceedings. PowerCon 2000. International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-6338-8
  • Type

    conf

  • DOI
    10.1109/ICPST.2000.897176
  • Filename
    897176