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

Hiroyuki Mori, Yoshinori Sakatani, Tatsurou Fujino, Kazuyuki Numa

研究成果: Article


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.

ジャーナルElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
出版物ステータスPublished - 30 7 2006

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