  Short-term wind speed prediction is essential for economical wind power
utilization. The real-world wind speed data is typically intermittent and
fluctuating, presenting great challenges to existing shallow models. In this
paper, we present a novel deep hybrid model for multi-step wind speed
prediction, namely LR-FFT-RP-MLP/LSTM (Linear Fast Fourier Transformation Rank
Pooling Multiple-Layer Perception/Long Short-Term Memory). Our hybrid model
processes the local and global input features simultaneously. We leverage Rank
Pooling (RP) for the local feature extraction to capture the temporal structure
while maintaining the temporal order. Besides, to understand the wind periodic
patterns, we exploit Fast Fourier Transformation (FFT) to extract global
features and relevant frequency components in the wind speed data. The
resulting local and global features are respectively integrated with the
original data and are fed into an MLP/LSTM layer for the initial wind speed
predictions. Finally, we leverage a linear regression layer to collaborate
these initial predictions to produce the final wind speed prediction. The
proposed hybrid model is evaluated using real wind speed data collected from
2010 to 2020, demonstrating superior forecasting capabilities when compared to
state-of-the-art single and hybrid models. Overall, this study presents a
promising approach for improving the accuracy of wind speed forecasting.
