Advertisement

NWP Ensembles for Wind Energy Uncertainty Estimates

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)

Abstract

Numerical weather predictions (NWP) ensembles, i.e., probabilistic variants of NWP forecasts, can be a useful tool to improve the quality of renewable energy predictions as well as to provide useful estimates of uncertainty in NWP–based energy forecasts. In this work we will consider the application of the NWP ensembles provided by the European Center for Medium Weather Forecasts (ECMWF) to deal with these issues. We shall consider both local prediction at a single wind farm as well as the wide area prediction of wind energy over Peninsular Spain and show that while deterministic forecasts have an edge over ensemble based ones, these can be used to derive quite good uncertainty intervals.

Keywords

Numerical weather prediction Probabilistics ensembles Wind energy Multilayer perceptrons Support vector regression Uncertainty estimates 

Notes

Acknowledgments

With partial support from Spain’s grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM; work also supported by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016 and the UAM–ADIC Chair for Data Science and Machine Learning. The first author is kindly supported by the UAM-ADIC Chair for Data Science and Machine Learning. We gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy data. We also thank the ECMWF and Spain’s AEMET for kindly granting access to the MARS repository.

References

  1. 1.
    Guidelines on ensemble prediction systems and forecasting. Tech. Rep. WMO-No: 1091, World Meteorological Organization (2012)Google Scholar
  2. 2.
    Alonso, Á., Torres, A., Dorronsoro, J.R.: Random forests and gradient boosting for wind energy prediction. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 26–37. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19644-2_3 CrossRefGoogle Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)MATHGoogle Scholar
  4. 4.
    Chollet, F.: Keras: Deep learning library for theano and tensorflow (2015), http://keras.io
  5. 5.
    Díaz-Vico, D., Torres-Barrán, A., Omari, A., Dorronsoro, J.R.: Deep neural networks for wind and solar energy prediction. Neural Process. Lett., April 2017Google Scholar
  6. 6.
    Fang, S., Chiang, H.D.: A high-accuracy wind power forecasting model. IEEE Trans. Power Syst. 32(2), 1589–1590 (2017)Google Scholar
  7. 7.
    Giebel, G., Badger, J., Landberg, L., Henrik, Nielsen, A., Nielsen, T., Madsen, H., Sattler, K., Feddersen, H., Vedel, H., Tøfting, J., Kruse, L., Voulund, L.: Wind power prediction using ensembles. Tech. Rep. R-1527, Risø Risø National Laboratory Roskilde, Denmark (2005)Google Scholar
  8. 8.
    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 2010Google Scholar
  9. 9.
    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 2011Google Scholar
  10. 10.
    Heinermann, J.: Wind Power Prediction with Machine Learning Ensembles. Ph.D. thesis, Carl Von Ossietzky University, Oldenburg, Germany (2016)Google Scholar
  11. 11.
    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014), http://arxiv.org/abs/1412.6980
  12. 12.
    Leutbecher, M., Palmer, T.N.: Ensemble forecasting. J. Comput. Phys. 227(7), 3515–3539 (2008)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Lin, C.J., Weng, R.C.: Simple probabilistic predictions for support vector regression. Techical Report, Department of Computer Science, National Taiwan University (2003)Google Scholar
  14. 14.
    Mohammed, A.A., Yaqub, W., Aung, Z.: Probabilistic forecasting of solar power: an ensemble learning approach. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. SIST, vol. 39, pp. 449–458. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19857-6_38 Google Scholar
  15. 15.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MATHMathSciNetGoogle Scholar
  16. 16.
    Ren, Y., Suganthan, P., Srikanth, N.: Ensemble methods for wind and solar power forecasting-a state-of-the-art review. Renew. Sustain. Energy Rev. 50, 82–91 (2015)CrossRefGoogle Scholar
  17. 17.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2001)Google Scholar
  18. 18.
    Sperati, S., Alessandrini, S., Delle Monache, L.: An application of the ECMWF ensemble prediction system for short-term solar power forecasting. Sol. Energy 133, 437–450 (2016)CrossRefGoogle Scholar
  19. 19.
    Woon, W.L., Kramer, O.: Enhanced SVR ensembles for wind power prediction. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2743–2748 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Dpto. Ing. Informática and Instituto de Ingeniería del ConocimientoUniversidad Autónoma de MadridMadridSpain

Personalised recommendations