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Conference paper
Deep neural networks for ultra-short-term wind forecasting
IEEE International Conference on Industrial Technology, ICIT 2015 Sevilla, pp.1657-1663, 2015
ABSTRACT:
The aim of this paper is to present input variable selection algorithm and deep neural networks application to ultrashort-term wind prediction. Shallow and deep neural networks coupled with input variable selection algorithm are compared on the ultra-short-term wind prediction task for a set of different locations. Results show that carefully selected deep neural networks outperform shallow ones. Input variable selection use reduces the neural network complexity and simplifies deep neural network training.
BibTeX entry:
@inproceedings \{Dalto2015_556,
author = \{Djalto, M. AND Matu\v{s}ko, J. AND Va\v{s}ak, M.},
title = \{Deep neural networks for ultra-short-term wind forecasting},
booktitle = {IEEE International Conference on Industrial Technology, ICIT 2015 Sevilla},
pages = \{1657-1663},
year = \{2015}
}

 

 

 

 

 

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