書誌事項
- タイトル別名
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- Reconstructing Clusters for Preconditioned Short-term Load Forecasting
- 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
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抄録
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.
収録刊行物
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- 電気学会論文誌B(電力・エネルギー部門誌)
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電気学会論文誌B(電力・エネルギー部門誌) 125 (3), 302-308, 2005
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679579829888
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- NII論文ID
- 10014490605
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- NII書誌ID
- AN10136334
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- ISSN
- 13488147
- 03854213
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- NDL書誌ID
- 7270146
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可