The accurate wind speed series forecast is very pivotal to security of grid dispatching and the application of wind power. Nevertheless, on account of their nonlinear and non-stationary nature, their short-term forecast is extremely challenging. Therefore, this dissertation raises one short-term wind speed forecast pattern on the foundation of attention with an improved gated recurrent neural network (AtGRU) and a tactic of error correction. That model uses the AtGRU model as the preliminary predictor and the GRU model as the error corrector. At the beginning, SSA (singular spectrum analysis) is employed in previous wind speed series for lessening the noise. Subsequently, historical wind speed series is going to be used for the predictor training. During this process, the prediction can have certain errors. The sequence of these errors processed by variational modal decomposition (VMD) is used to train the corrector of error. The eventual forecast consequence is just the sum of predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model outperforms the compared models in three case studies on Woodburn, St. Thomas, and Santa Cruz. It is indicated that the model evidently enhances the correction of the wind speed forecast.

翻译：准确的风速序列预测对电网调度的安全性和风电应用至关重要。然而，由于风速序列的非线性和非平稳特性，其短期预测极具挑战性。为此，本文提出一种基于注意力机制改进门控循环神经网络（AtGRU）及误差校正策略的短期风速预测模型。该模型以AtGRU作为初步预测器，GRU作为误差校正器。首先，采用奇异谱分析（SSA）对历史风速序列进行降噪处理；随后，利用降噪后的历史风速序列训练预测器，此过程中预测将产生一定误差。经变分模态分解（VMD）处理的误差序列将用于训练误差校正器，最终预测结果为预测器输出与误差校正器输出之和。在Woodburn、St. Thomas和Santa Cruz三个案例研究中，所提出的SSA-AtGRU-VMD-GRU模型均优于对比模型。结果表明，该模型显著提升了风速预测的准确性。