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.
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References
Guidelines on ensemble prediction systems and forecasting. Tech. Rep. WMO-No: 1091, World Meteorological Organization (2012)
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
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)
Chollet, F.: Keras: Deep learning library for theano and tensorflow (2015), http://keras.io
Díaz-Vico, D., Torres-Barrán, A., Omari, A., Dorronsoro, J.R.: Deep neural networks for wind and solar energy prediction. Neural Process. Lett., April 2017
Fang, S., Chiang, H.D.: A high-accuracy wind power forecasting model. IEEE Trans. Power Syst. 32(2), 1589–1590 (2017)
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)
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
Heinermann, J.: Wind Power Prediction with Machine Learning Ensembles. Ph.D. thesis, Carl Von Ossietzky University, Oldenburg, Germany (2016)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014), http://arxiv.org/abs/1412.6980
Leutbecher, M., Palmer, T.N.: Ensemble forecasting. J. Comput. Phys. 227(7), 3515–3539 (2008)
Lin, C.J., Weng, R.C.: Simple probabilistic predictions for support vector regression. Techical Report, Department of Computer Science, National Taiwan University (2003)
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
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)
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)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2001)
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)
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)
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.
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Catalina, A., Dorronsoro, J.R. (2017). NWP Ensembles for Wind Energy Uncertainty Estimates. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. DARE 2017. Lecture Notes in Computer Science(), vol 10691. Springer, Cham. https://doi.org/10.1007/978-3-319-71643-5_11
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