LSTM-Based Multi-scale Model for Wind Speed Forecasting

  • Ignacio A. ArayaEmail author
  • Carlos Valle
  • Héctor Allende
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Wind speed forecasting is crucial for the penetration of wind energy sources in electrical systems, since accurate wind speed forecasts directly translates into accurate wind power predictions. A framework called Multi-scale RNNs specifically addresses the issue of learning long term dependencies in RNNs. Following that approach, we devised a LSTM-based Multi-scale model that learns to build different temporal scales from the original wind speed series that are then used as input for multiple LSTMs, whose final internal states are used to forecast wind speed future values. Results from two real wind speed datasets from northern Chile show that this approach outperforms the standard LSTM and its capable of working with very long input series without overfitting, while being computationally efficient regarding training times.


Wind speed forecasting Long Short-Term Memory Multi-scale recurrent networks 



This work was supported in part by Fondecyt Grant 1170123, Basal Project FB0821 and the PIIC Research Project, DGIIP-UTFSM (Chile).


  1. 1.
    Hallas, M., Dorffner, G.: A comparative study on feedforward and recurrent neural networks in time series prediction using gradient descent learning (1998)Google Scholar
  2. 2.
    Barbounis, T.G., Theocharis, J.B., Alexiadis, M.C., Dokopoulos, P.S.: Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans. Energy Convers. 21(1), 273–284 (2006)CrossRefGoogle Scholar
  3. 3.
    Cao, Q., Ewing, B.T., Thompson, M.A.: Forecasting wind speed with recurrent neural networks. Eur. J. Oper. Res. 221(1), 148–154 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wang, J., Zhang, W., Li, Y., Wang, J., Dang, Z.: Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl. Soft Comput. 23, 452–459 (2014)CrossRefGoogle Scholar
  5. 5.
    Liu, H., Tian, H.Q., Liang, X.F., Li, Y.F.: Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl. Energy 157, 183–194 (2015)CrossRefGoogle Scholar
  6. 6.
    Liu, H., Mi, X.W., Li, Y.F.: Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers. Manag. 156, 498–514 (2018)CrossRefGoogle Scholar
  7. 7.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  8. 8.
    Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In International Conference on Machine Learning, pp. 1310–1318, February 2013Google Scholar
  9. 9.
    Chung, J., Ahn, S., Bengio, Y.: Hierarchical multiscale recurrent neural networks. arXiv preprint arXiv:1609.01704 (2016)
  10. 10.
    Koutnik, J., Greff, K., Gomez, F., Schmidhuber, J.: A clockwork RNN. In: International Conference on Machine Learning, pp. 1863–1871, January 2014Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ignacio A. Araya
    • 1
    Email author
  • Carlos Valle
    • 2
  • Héctor Allende
    • 1
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Departamento de Computación e InformáticaUniversidad de Playa AnchaValparaísoChile

Personalised recommendations