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Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

机译：基于多元数据二次分解方法的混合风速预测模型和注意力机制的深度学习算法

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摘要

Accurate and reliable wind speed forecasting is important for the dispatch and management of wind power generation systems. However, existing forecasting models based on the data decomposition approach only perform time-frequency analysis of wind speed series, while ignoring the coupling relationship between other meteorological variables and wind speed in the time and frequency domains. Therefore, a novel hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with an attention mechanism is proposed in this study. Specifically, singular spectrum analysis is used to decrease the noise of the original multivariate series. Multivariate empirical mode decomposition is used to decompose the denoised series into their respective intrinsic mode functions and residuals. Further, a new hybrid deep learning algorithm, which combines a convolutional neural network optimized via an attention mechanism and a bidirectional long short-term memory network, is proposed to extract spatiotemporal correlation features between all intrinsic mode functions and residuals and to perform final wind speed forecasting. Finally, three experiments were performed to evaluate the performance of the proposed model comprehensively. The experimental results indicate that the proposed model is superior to other baseline models in terms of both accuracy and effectiveness.(c) 2021 Elsevier Ltd. All rights reserved.
机译：准确可靠的风速预测对于发电系统的调度和管理很重要。然而，基于数据分解方法的现有预测模型仅对风速系列进行时频分析，同时在时间和频率域中忽略其他气象变量与风速之间的耦合关系。因此，在本研究中提出了一种基于多变量数据二次分解方法的新型混合风速预测模型和注意机制的深度学习算法。具体地，奇异频谱分析用于降低原始多元系列的噪声。多变量经验模式分解用于将去噪的系列分解为各自的内在模式函数和残差。此外，提出了一种新的混合深度学习算法，其通过注意机制和双向短期存储网络优化的卷积神经网络，以提取所有内在模式功能和残差之间的时空相关特征，并执行最终风速预测。最后，进行了三个实验，以综合评估所提出的模型的性能。实验结果表明，在精度和有效性方面，该模型优于其他基线模型。（c）2021 elestvier有限公司保留所有权利。

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