Long Short-Term Memory Networks Based in Echo State Networks for Wind Speed Forecasting

  • Erick López
  • Carlos Valle
  • Héctor Allende
  • Esteban Gil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

Integrating increasing amounts of wind generation require power system operators to improve their wind forecasting tools. Echo State Networks (ESN) are a good option for wind speed forecasting because of their capacity to process sequential data, having achieved good performance in different forecasting tasks. However, the simplicity of not training its hidden layer may restrict reaching a better performance. This paper proposes to use an ESN architecture, but replacing its hidden units by LSTM blocks and to train the whole network with some restrictions. We tested the proposal by forecasting wind speeds from 1 to 24 h ahead. Results demonstrate that our proposal outperforms the ESNs performance in terms of different error metrics such as MSE, MAE and MAPE.

Keywords

Wind speed forecasting Echo State Network Long Short-Term Memory Network Multivariate time series 

Notes

Acknowledgments

This work was supported in part by Fondecyt 1170123 and in part by Basal Project FB0821.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Erick López
    • 1
  • Carlos Valle
    • 1
  • Héctor Allende
    • 1
  • Esteban Gil
    • 2
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.Departamento de Ingeniería EléctricaUniversidad Técnica Federico Santa MaríaValparaísoChile

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