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Feature extraction of one‐step‐ahead daily maximum load with regression tree
Author(s) -
Mori Hiroyuki,
Sakatani Yoshinori,
Fujino Tatsurou,
Numa Kazuyuki
Publication year - 2006
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20104
Subject(s) - computer science , data mining , variable (mathematics) , tree (set theory) , regression , decision tree , feature (linguistics) , regression analysis , artificial intelligence , machine learning , statistics , mathematics , mathematical analysis , linguistics , philosophy
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. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(2): 43–51, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20104