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

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

Editorial: Springer

ISSN: 0302-9743,  1611-3349

ISBN: 9783319192574,  9783319192581

Año de publicación: 2015

Páginas: 430-443

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

Tipo: Aportación congreso

DOI: 10.1007/978-3-319-19258-1_36 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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.

Referencias bibliográficas

  • European center for medium-range weather forecasts. http://www.ecmwf.int/
  • Global forecast system. http://www.emc.ncep.noaa.gov/index.php?branch=gfs
  • Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I.J., Bergeron, A., Bouchard, N., Bengio, Y.: Theano: new features and speed improvements. In: Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop (2012)
  • Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems 19 (NIPS 2006), pp. 153–160 (2007). http://www.iro.umontreal.ca/~lisa/pointeurs/BengioNips2006All.pdf
  • Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation
  • Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011). software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
  • Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, Barcelona, Catalonia, Spain, July 16–22, 2011, pp. 1237–1242 (2011). http://ijcai.org/papers11/Papers/IJCAI11-210.pdf
  • Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: JMLR W&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), vol. 9, pp. 249–256, May 2010
  • Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: JMLR W&CP: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011), April 2011
  • Goodfellow, I.J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., Bengio, Y.: Pylearn2: a machine learning research library. arXiv preprint arXiv:1308.4214 (2013). http://arxiv.org/abs/1308.4214
  • He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR abs/1502.01852 (2015). http://arxiv.org/abs/1502.01852
  • Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). http://www.sciencemag.org/content/313/5786/504.abstract
  • Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. CoRR abs/1408.5093 (2014). http://arxiv.org/abs/1408.5093
  • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  • Kruger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., Rodriguez-Sanchez, A., Wiskott, L.: Deep hierarchies in the primate visual cortex: What can we learn for computer vision? IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1847–1871 (2013)
  • LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
  • LeCun, Y., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998)
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1929–1958 (2014). http://jmlr.org/papers/v15/srivastava14a.html
  • Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: On the importance of initialization and momentum in deep learning. In: Dasgupta, S., Mcallester, D. (eds.) Proceedings of the 30th International Conference on Machine Learning (ICML 2013), vol. 28, pp. 1139–1147. JMLR Workshop and Conference Proceedings, May 2013. http://jmlr.org/proceedings/papers/v28/sutskever13.pdf