An Ensemble GRU Approach for Wind Speed Forecasting with Data Augmentation

Abstract

This paper proposes an ensemble model for wind speed forecasting using the recurrent neural network known as Gated Recurrent Unit (GRU) and data augmentation. For the experimentation, a single wind speed time series is used, from which four augmented time series are generated, which serve to train four GRU sub-models respectively, the results of these submodels are averaged to generate the results of the proposal ensemble model (E-GRU). The results achieved by E-GRU are compared with those of each sub-model, showing that E-GRU outperforms the sub-models. Likewise, the proposal model (E-GRU) is compared with benchmark models without data augmentation such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), showing that E-GRU is much more precise, reaching a difference of around 15% with respect to the Relative Root mean Square Error (RRMSE) and 11% with respect to the Mean Absolute Percentage Error (MAPE).