LSTM-based multi-scale model for wind speed forecasting

Wind speed forecasting is crucial for the penetration of wind energy sources in electrical systems, since accurate wind speed forecasts directly translates into accurate wind power predictions. A framework called Multi-scale RNNs specifically addresses the issue of learning long term dependencies in RNNs. Following that approach, we devised a LSTM-based Multi-scale model that learns to build different temporal scales from the original wind speed series that are then used as input for multiple LSTMs, whose final internal states are used to forecast wind speed future values. Results from two real wind speed datasets from northern Chile show that this approach outperforms the standard LSTM and its capable of working with very long input series without overfitting, while being computationally efficient regarding training times.