论文标题
多步短期风速预测，汇总和快速的傅立叶变换
Multi-Step Short-Term Wind Speed Prediction with Rank Pooling and Fast Fourier Transformation
论文作者
论文摘要
短期风速预测对于经济风能利用至关重要。现实世界中的风速数据通常是间歇性和波动的，对现有浅层模型带来了巨大挑战。在本文中，我们提出了一种用于多步风速预测的新型深层混合模型，即LR-FFT-RP-MLP/LSTM（线性快速傅立叶变换层汇总多层感知/长短期记忆）。我们的混合模型同时处理本地和全局输入功能。我们利用局部特征提取的层次池（RP）来捕获时间结构，同时保持时间顺序。此外，要了解风周期模式，我们利用快速的傅立叶变换（FFT）来提取风速数据中的全局特征和相关的频率组件。所得的本地和全局特征分别与原始数据集成在一起，并馈入MLP/LSTM层以进行初始风速预测。最后，我们利用线性回归层协作这些初始预测以产生最终的风速预测。使用从2010年到2020年收集的实际风速数据评估了提出的混合模型，与最先进的单个和混合模型相比，表明了卓越的预测能力。总体而言，这项研究提出了提高风速预测准确性的有希望的方法。
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.
