Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast
Fourier Transformation
Abstract
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.
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