NWP Ensembles for Wind Energy Uncertainty Estimates

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


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.


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



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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© Springer International Publishing AG 2017

Authors and Affiliations

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

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