Wind power 24-h ahead forecast by an artificial neural network and an hybrid model: Comparison of the predictive performance

In the last decade, wind has experienced a strong expansion reaching 591 GW (2018) of installed capacity worldwide. The higher penetration of variable renewable energy sources (wind and solar) has led to a growing demand for reliable forecast methods, to properly integrate these sources in the electric grid, decreasing the cost of electricity production and power curtailments. The present work proposes diverse wind power predictive approaches based on a physical model, artificial neural networks and an hybridization of the two. The time series used is composed of two-years hourly measurements of a wind farm in Italy, consisting of 24 wind turbines with a nominal power of 0.66 MW. To ensure an adequate reliability and robustness of the results obtained from the performance evaluation, it was chosen to use eight different error metrics and to evaluate the accuracy considering two different predictive situations (yearly and daily), using the persistence model as benchmark. The evaluations of predictive performances, regarding both the analyses, confirmed the superiority of data-driven approaches in the daily wind power prediction. More in detail, the hybrid model managed to reduce the MAE, the NRMSE and the SS values, compared to persistence, by 50%, 47.82% and 47.68%, respectively.