Solar radiation prediction with deep learning and data augmentation

In this work a model for the prediction of daily solar radiation time series is proposed, this model is based on recurrent neural networks (RNN) and data augmentation. The RNN used is Long Short-Term Memory (LSTM), and the data augmentation technique is based on time-warping and jittering. It is experimented with data of 6 years,5 years for training and 1 year for testing. The results show that the proposal model with data augmentation (DA+LSTM) considerably outperforms an LSTM model without data augmentation, as well as other state-of-the-art models with which it was compared.