Reconstructing Clusters for Preconditioned Short-term Load Forecasting

• Itagaki Tadahiro

  Dept. of Electrical and Electronics Eng., Meiji University

• Mori Hiroyuki

  Dept. of Electrical and Electronics Eng., Meiji University

Bibliographic Information

Other Title

• 短期電力負荷予測におけるクラスタ再構成前処理手法
• タンキ デンリョク フカ ヨソク ニ オケル クラスタ サイコウセイ マエ ショリ シュホウ

Abstract

This paper presents a new preconditioned method for short-term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short-term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen-based neural network. That enhances a short-term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.

Journal

• IEEJ Transactions on Power and Energy

  IEEJ Transactions on Power and Energy 125 (3), 302-308, 2005

  The Institute of Electrical Engineers of Japan

Keywords

• short-term load forecasting
• clustering
• neural network
• self organizing maps
• radial basis function network

Details

• CRID: 1390282679579829888
• NII Article ID: 10014490605
• NII Book ID: AN10136334
• DOI: 10.1541/ieejpes.125.302
• ISSN: 13488147
• ISSN: 03854213
• NDL BIB ID: 7270146
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7270146
• Web Site: http://www.jstage.jst.go.jp/article/ieejpes/125/3/125_3_302/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed