A Data Mining Method for Short-term Load Forecasting in Power Systems

• Mori Hiroyuki

  Meiji University

• Kosemura Noriyuki

  Meiji University

Bibliographic Information

Other Title

• データマイニング手法による短期電力負荷予測
• データ マイニング シュホウ ニ ヨル タンキ デンリョク フカ ヨソク

Abstract

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 to clarify the cause and effect of dynamic load behavior in load forecasting. However, the regression tree does not necessarily give 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 multi-layer perceptron of a universal nonlinear approximator. The effectiveness of the proposed method is demonstrated in real data.

Journal

• IEEJ Transactions on Power and Energy

  IEEJ Transactions on Power and Energy 121 (2), 234-241, 2001

  The Institute of Electrical Engineers of Japan

Keywords

• short-term load forecasting
• data mining
• regression tree
• neural net
• multi-layer perceptron

Details

• CRID: 1390001204604849920
• NII Article ID: 130006841295
• NII Article ID: 10005721949
• NII Book ID: AN10136334
• DOI: 10.1541/ieejpes1990.121.2_234
• ISSN: 13488147
• ISSN: 03854213
• ISSN: http://id.crossref.org/issn/03854213
• NDL BIB ID: 5657523
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I5657523
• Web Site: https://www.jstage.jst.go.jp/article/ieejpes1990/121/2/121_2_234/_pdf
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed