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Journal of Signal Processing
Vol. 21 (2017) No. 4 Special Issue on Papers Awarded the Student Paper Award at NCSP'17 (Editor-in-Chief: Keikichi Hirose, Editor: Yoshikazu Miyanaga, Guest Editor: Masashi Unoki, Honorary Editor-in-Chief: Takashi Yahagi) p. 183-186



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.

Copyright © 2017 Research Institute of Signal Processing, Japan

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