Reconstructing Clusters for Preconditioned Short-term Load Forecasting

Tadahiro Itagaki, Hiroyuki Mori

Research output: Contribution to journalArticle

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)302-308
Number of pages7
JournalIEEJ Transactions on Power and Energy
Issue number3
Publication statusPublished - 1 Jan 2005



  • clustering
  • neural network
  • radial basis function network
  • self organizing maps
  • short-term load forecasting

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