Feature extraction of one-step-ahead daily maximum load with regression tree

Hiroyuki Mori, Yoshinori Sakatani, Tatsurou Fujino, Kazuyuki Numa

Research output: Contribution to journalArticle


In this paper, a new efficient feature extraction method is proposed to handle the one-step-ahead daily maximum load forecasting. In recent years, power systems become more complicated under the deregulated and competitive environment. As a result, it is not easy to understand the cause and effect of short-term load forecasting with a bunch of data. This paper analyzes load data from the standpoint of data mining. By it we mean a technique that finds out rules or knowledge through large database. As a data mining method for load forecasting, this paper focuses on the regression tree that handles continuous variables and expresses a knowledge rule as if-then rules. Investigating the variable importance of the regression tree gives information on the transition of the load forecasting models. This paper proposes a feature extraction method for examining the variable importance. The proposed method allows to classify the transition of the variable importance through actual data.

Original languageEnglish
Pages (from-to)43-51
Number of pages9
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Issue number2
Publication statusPublished - 30 Jul 2006



  • Data mining
  • Feature extraction
  • Load forecasting
  • Regression tree
  • Risk management
  • Variable importance

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