A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy

This work presents a novel transformer-based deep neural network architecture integrated with wavelet transform for forecasting wind speed and wind energy (power) generation for the next 6 h ahead, using multiple meteorological variables as input for multivariate time series forecasting. To evaluate the performance of the proposed model, different case studies were investigated, using data collected from anemometers installed in three different regions in Bahia, Brazil. The performance of the proposed transformer-based model with wavelet transform was compared with an LSTM (Long Short Term Memory) model as a baseline, since it has been successfully used for time series processing in deep learning, as well as…