Reconstructing Clusters for Preconditioned Short-term Load Forecasting

Tadahiro Itagaki, Hiroyuki Mori

研究成果: Article

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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.

元の言語English
ページ(範囲)302-308
ページ数7
ジャーナルIEEJ Transactions on Power and Energy
125
発行部数3
DOI
出版物ステータスPublished - 1 1 2005

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