GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting

Wind energy is getting more and more integrated into power grids, giving rise to some challenges because of its inherent intermittent and irregular nature. Wind speed forecasting plays a fundamental role in overcoming such challenging issues and, thus, assisting the power utility manager in optimizing the supply-demand balancing through wind energy generation. This paper suggests a new hybrid scheme WNN, based on discrete wavelet transform (DWT) combined with artificial neural network (ANN), for wind speed forecasting. More specifically, this work aims at designing the most appropriate discrete wavelet filters, best adapted to a one day ahead wind speed forecasting. The optimized DWT filters are intended to effectively preprocess the wind speed time series data in order to enhance the prediction accuracy. Using wind speed data collected from three different locations in the Magherbian region, the obtained simulation results indicate that the proposed approach outperforms other conventional wavelet-based forecasting structures regarding the wind speed prediction precision. Moreover, compared to the standard wavelet 'db4' based approach, the optimized wavelet filter-based structure leads to a forecasting accuracy improvement, in terms of RMSE and MAPE index errors, that amounts to nearly 13% and 19%, respectively.