Comparison of Recurrent Neural Networks for Wind Power Forecasting

López, Erick; Valle, Carlos; Allende-Cid, Héctor; Allende, Héctor

Integrating wind power to the electrical grid is complicated due to the stochastic nature of the wind, which makes its prediction a challenging task. Then, it is important to devise forecasting tools to support this task. For example, a network that integrates an Echo State Network architecture and Long Short-Term Memory blocks as hidden units (ESN+LSTM) has been proposed, showing good performance against a physical model. This paper proposes to compare this network versus Echo State Network (ESN) and Long Short-Term Memory (LSTM), to forecast wind power from 1 to 24 h ahead. Results show than the ESN+LSTM model outperforms the performance reached for ESN and LSTM, in terms of MSE, MAE, and the metrics used in the Taylor diagram. In addition, we observe that the advantage of this network is statistically significant during the first moments of the forecast horizon, in terms of T-test and Wilcoxon-test.