Long Short-Term Memory Networks Based in Echo State Networks for Wind Speed Forecasting

Abstract

Integrating increasing amounts of wind generation require power system operators to improve their wind forecasting tools. Echo State Networks (ESN) are a good option for wind speed forecasting because of their capacity to process sequential data, having achieved good performance in different forecasting tasks. However, the simplicity of not training its hidden layer may restrict reaching a better performance. This paper proposes to use an ESN architecture, but replacing its hidden units by LSTM blocks and to train the whole network with some restrictions. We tested the proposal by forecasting wind speeds from 1 to 24 h ahead. Results demonstrate that our proposal outperforms the ESNs performance in terms of different error metrics such as MSE, MAE and MAPE.