Premium
A data mining method for short‐term load forecasting in power systems
Author(s) -
Mori Hiroyuki,
Kosemura Noriyuki
Publication year - 2002
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.1150
Subject(s) - computer science , artificial neural network , decision tree , data mining , multilayer perceptron , term (time) , regression , electric power system , tree (set theory) , perceptron , artificial intelligence , regression analysis , machine learning , power (physics) , statistics , mathematics , physics , quantum mechanics , mathematical analysis
This paper proposes a method for daily maximum load forecasting in power systems. It is based on the integration of the regression tree and the artificial neural network. In this paper, the regression tree is used to extract knowledge or rules as a data‐mining method. That is useful for the information processing of the complicated data. As a result, the proposed method has an advantage in clarifying the cause and effect of dynamic load behavior in load forecasting. However, the regression tree does not necessarily yield good prediction results in spite of good classification. Therefore, this paper proposes a method for combining the classification results of the regression tree with the multilayer perceptron of a universal nonlinear approximator. The effectiveness of the proposed method is demonstrated in real data. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 12–22, 2002; DOI 10.1002/eej.1150