Deep Neural Networks for Wind Energy Prediction

  1. Díaz, David 1
  2. Torres, Alberto 1
  3. Dorronsoro, José R. 1
  1. 1 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

Proceedings:
13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Proceedings, Part I (Advances in Computational Intelligence)

Publisher: Springer

ISSN: 0302-9743,  1611-3349

ISBN: 9783319192574,  9783319192581

Year of publication: 2015

Pages: 430-443

Congress: 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part I

Type: Conference paper

DOI: 10.1007/978-3-319-19258-1_36 GOOGLE SCHOLAR lock_openOpen access editor

Abstract

In this work we will apply some of the Deep Learning models that are currently obtaining state of the art results in several machine learning problems to the prediction of wind energy production. In particular, we will consider both deep, fully connected multilayer perceptrons with appropriate weight initialization, and also convolutional neural networks that can take advantage of the spatial and feature structure of the numerical weather prediction patterns. We will also explore the effects of regularization techniques such as dropout or weight decay and consider how to select the final predictive deep models after analyzing their training evolution.

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