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

TL;DR

This paper introduces a novel hybrid deep learning model combining Rank Pooling and Fast Fourier Transformation to improve multi-step short-term wind speed prediction accuracy by capturing local and global features.

Contribution

The paper proposes a new hybrid deep model integrating Rank Pooling and FFT with MLP/LSTM layers for enhanced wind speed forecasting accuracy.

Findings

The hybrid model outperforms state-of-the-art models on real wind data.

Incorporating local and global features improves prediction accuracy.

The approach effectively captures wind speed periodic patterns.

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.