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Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast Fourier Transformation
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  • Hailong Shu,
  • Weiwei SONG,
  • Zhen SONG,
  • Huichuang GUO,
  • Chaoqun LI
Hailong Shu
State Key Laboratory of NBC Protection for Civilian

Corresponding Author:shl@pku.edu.cn

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Weiwei SONG
State Key Laboratory of NBC Protection for Civilian
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Zhen SONG
State Key Laboratory of NBC Protection for Civilian
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Huichuang GUO
State Key Laboratory of NBC Protection for Civilian
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Chaoqun LI
State Key Laboratory of NBC Protection for Civilian
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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.
Submitted to Wind Energy
14 Aug 20231st Revision Received
22 Aug 2023Assigned to Editor
22 Aug 2023Submission Checks Completed
22 Aug 2023Review(s) Completed, Editorial Evaluation Pending
27 Aug 2023Reviewer(s) Assigned
08 Oct 2023Editorial Decision: Revise Major
02 Mar 2024Review(s) Completed, Editorial Evaluation Pending
03 Mar 2024Editorial Decision: Accept