| Título: | Deep Neural Networks for Wind and Solar Energy Prediction. |
|---|---|
| Autores: | Díaz Vico, D., Torres Barrán, A., Omari, A., & Dorronsoro, J. |
| Año: | 2017 |
| Enlace: | https://link.springer.com/article/10.1007%2Fs11063-017-9613-7 |
Deep Learning models are recently receiving a large attention because of their very powerful modeling abilities, particularly on inputs that have an 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.
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