Deep Neural Networks for Wind and Solar Energy Prediction
EntityUAM. Departamento de Ingeniería Informática
10.1007/s11063-017-9613-7Díaz–Vico, D., Torres–Barrán, A., Omari, A., Dorronsoro, José R., Deep Neural Networks for Wind and Solar Energy Prediction. Neural Processing Letters 46 (2017): 829-844
ISSN1573-773X (online); 1370-4621 (print)
Funded byWith partial support from Spain’s Grants TIN2013-42351-P (MINECO), S2013/ICE2845 CASI-CAM-CM (Comunidad de Madrid), FACIL (Ayudas Fundación BBVA a Equipos de Investigación Científica 2016) and the UAM–ADIC Chair for Data Science and Machine Learning. The second author is also kindly supported by the FPU-MEC Grant AP-2012-5163. The authors gratefully acknowledge access to the MARS repository granted by the ECMWF, the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy production data and to Sotavento for making their production data publicly available.
ProjectGobierno de España. TIN2013-42351-P; Comunidad de Madrid. S2013/ICE2845/CASI-CAM-CM
SubjectsConvolutional neural network; Deep learning; Solar energy; Wind energy; Informática
Rights© Springer Nature
Deep Learning models are recently receiving a large attention because of their very powerful modeling abilities, particularly on inputs that have a intrinsic one- or two-dimensional structure that can be captured and exploited by convolutional layers. In this work we will apply Deep Neural Networks (DNNs) in two problems, wind energy and daily solar radiation prediction, whose inputs, derived from Numerical Weather Prediction systems, have a clear spatial structure. As we shall see, the predictions of single deep models and, more so, of DNN ensembles can improve on those of Support Vector Regression, a Machine Learning method that can be considered the state of the art for regression
Google Scholar:Díaz–Vico, David - Torres–Barrán, Alberto - Omari, Adil - Dorronsoro Ibero, José Ramón
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