Deep neural networks for wind energy prediction
EntityUAM. Departamento de Ingeniería Informática
10.1007/978-3-319-19258-1_36Deep Neural Networks for Wind Energy Prediction. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science 9094 (2015): 430-443
Funded byWith partial support from Spain’s grants TIN2013-42351-P (MINECO) and S2013/ICE-2845 CASI-CAM-CM (Comunidad de Madrid), 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 the use of the facilities of Centro de Computacion Cientıfica (CCC) at UAM and thank Red El´ectrica de Espa˜na for kindly supplying wind energy production data
ProjectGobierno de España. TIN2013-42351-P; Comunidad de Madrid. S2013/ICE-2845/CASI-CAM-CM
Rights© Springer Nature
In this work we will apply some of the Deep Learning models that are currently obtaining state of the art results in several machine learning problems to the prediction of wind energy production. In particular, we will consider both deep, fully connected multilayer perceptrons with appropriate weight initialization, and also convolutional neural networks that can take advantage of the spatial and feature structure of the numerical weather prediction patterns. We will also explore the effects of regularization techniques such as dropout or weight decay and consider how to select the final predictive deep models after analyzing their training evolution
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Díaz-Vico, David; Fernández, Angela; Dorronsoro, José R.