書誌事項
- タイトル別名
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- A Data Mining Method for Short-term Load Forecasting in Power Systems
- 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
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抄録
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.
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) 121 (2), 234-241, 2001
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
- 1390001204604849920
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- NII論文ID
- 130006841295
- 10005721949
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- NII書誌ID
- AN10136334
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- ISSN
- 13488147
- 03854213
- http://id.crossref.org/issn/03854213
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- NDL書誌ID
- 5657523
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可