Prediction of Wind Speed Fluctuation Using Deep Belief Network with Ensemble Learning Method

• Yoshida Shogo

  Graduate School of Advanced Technology and Science, Tokushima University

• Suzuki Hiroshi

  Graduate School of Advanced Technology and Science, Tokushima University

• Kitajima Takahiro

  Graduate School of Advanced Technology and Science, Tokushima University

• Yasuno Takashi

  Graduate School of Advanced Technology and Science, Tokushima University

抄録

This paper describes a prediction method for wind speed fluctuation using a deep belief network (DBN) trained with ensemble learning. In particular, we investigate the usefulness of the ensemble learning for an prediction accuracy improvement of wind speed fluctuation. Bootstrap aggregating (the bagging method), which is a typical algorithm of ensemble learning, has been applied to train the DBN. The prediction result is decided by a majority vote of each DBN output. In addition, two bagging methods with different selection methods of training data have been proposed. These proposed methods have been evaluated from several prediction results by comparison with a conventional method.

収録刊行物

• 信号処理

  信号処理 21 (4), 183-186, 2017

  信号処理学会

詳細情報

• CRID: 1390001204463695488
• NII論文ID: 130005815310
• DOI: 10.2299/jsp.21.183
• ISSN: 18801013
• ISSN: 13426230
• Web Site: http://repo.lib.tokushima-u.ac.jp/118137
• Web Site: https://www.jstage.jst.go.jp/article/jsp/21/4/21_183/_pdf
• 本文言語コード: en
• データソース種別:
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