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Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network

机译：基于经验小波变换和新单元更新长短期记忆网络的风速预测方法

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摘要

Obtaining accurate wind speed forecast result plays a decisive role in ensuring the reliable operation of the power system integrated with large-scale wind power. Deep learning methods are increasingly being used to predict wind speed, which have relatively high prediction accuracy but more time-consuming training processes. The purposes of this study are further to improve the prediction accuracy of the wind speed and reduce the training time of the deep learning method. In this paper, a novel hybrid model, New Cell Update Long Short-Term Memory combined with Empirical Wavelet Transform, is proposed to increase the prediction accuracy in shorter training time. At first, the original wind speed sequence is preprocessed into a series of sub-sequence by the empirical wavelet decomposition. Then each sub-sequence is trained by New Cell Update Long Short-Term Memory which is proposed by this paper respectively and the sum of each sub-sequence is treated as the final prediction results. In order to verify the performance of the proposed model, different decomposition methods and different prediction methods are compared on the four actual wind speed prediction cases in the Inner Mongolia, China from prediction accuracy and training time. The results demonstrate that: (1) New Cell Update Long Short-Term Memory network has slightly higher prediction accuracy and shorter training time than Long Short-Term Memory network. (2) The prediction accuracy of the model is significantly improved after empirical wavelet decomposition. Therefore, New Cell Update Long Short-Term Memory network combined with empirical wavelet decomposition is a competitive wind speed prediction method compared to the existing state-ofthe-art approach.
机译：获得准确的风速预测结果对于确保集成有大规模风电的电力系统的可靠运行起着决定性的作用。深度学习方法正越来越多地用于预测风速，该方法具有相对较高的预测精度，但训练过程更加耗时。这项研究的目的是进一步提高风速的预测准确性并减少深度学习方法的训练时间。本文提出了一种新的混合模型，新细胞更新长短期记忆与经验小波变换相结合，以提高训练时间的预测精度。首先，通过经验小波分解将原始风速序列预处理为一系列子序列。然后分别由本文提出的新单元更新长短期记忆训练每个子序列，并将每个子序列的总和作为最终预测结果。为了验证所提模型的性能，从预测精度和训练时间出发，对中国内蒙古的四种实际风速预测案例进行了比较，对不同的分解方法和预测方法进行了比较。结果表明：（1）新的单元更新长短期记忆网络比长短期记忆网络具有更高的预测精度和较短的训练时间。 （2）经经验小波分解后，该模型的预测精度大大提高。因此，与现有的最新方法相比，结合经验小波分解的新单元更新长短期记忆网络是一种有竞争力的风速预测方法。

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