Nowcasting prediction of wind speed using computational intelligence and wavelet in Brazil.

This work presents a novel investigation on the nowcasting prediction of wind speed for three sites in Bahia, Brazil. For this, it was applied the computational intelligence by supervised machine learning using different artificial neural network technique, which was trained, validated, and tested using time series are derived from measurements that are acquired in towers equipped with anemometers at heights of 100.0, 120.0 and 150.0 m. To define the most efficient ANN, different topologies were tested using MLP and RNN, applying Wavelet packet decomposition (bior, coif, db, dmey, rbior, sym). The best statistical analysis was RNN + discrete Meyer wavelet. A new methodology for improving forecast accuracy of wind speed using artificial neural network (ANN) and Wavelet packet decomposition. Using machine learning and Wavelet packet decomposition to nowcast wind speed (m/s). To predict the wind speed at 100.0 m, 120.0 m and 150.0 m height in tropical region. Performance evaluation of Wavelet packet decomposition applying 48 different mother Wavelet functions. ANN approach for the estimation of nine types of wind speed time series. The proposed hybrid model (ANN + Wavelet packet decomposition) is capable of wind speed forecasting efficiently. [ABSTRACT FROM AUTHOR]