短期電力負荷予測におけるクラスタ再構成前処理手法  [in Japanese] Reconstructing Clusters for Preconditioned Short-term Load Forecasting  [in Japanese]

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Author(s)

Abstract

This paper presents a new preconditioned method for short-term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short-term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen-based neural network. That enhances a short-term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.

Journal

  • IEEJ Transactions on Power and Energy

    IEEJ Transactions on Power and Energy 125(3), 302-308, 2005-03-01

    The Institute of Electrical Engineers of Japan

References:  22

Cited by:  2

Codes

  • NII Article ID (NAID)
    10014490605
  • NII NACSIS-CAT ID (NCID)
    AN10136334
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    03854213
  • NDL Article ID
    7270146
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-794
  • Data Source
    CJP  CJPref  NDL  J-STAGE 
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