基于变分模式分解和深度门控循环网络的风速短期预测模型

考虑到风速时间序列非平稳特性和时序关联难以建模的问题，提出一种基于变分模态分解和深度门控循环网络的风速短期预测模型。该模型首先使用变分模态分解非递归地将原始风速序列分解为预先设定层数的子分量，以期降低原始序列的不平稳度，使用深度门控网络分别对各子分量建模预测，最后叠加各分量的预测结果，得到风速的预测结果。实例研究表明所提模型能够有效地跟踪风速的变化，具有较高的短期预测精度。

Short-Term Wind Speed Prediction Based on VMD and Deep GRU Network

Considering that it is difficult to model the time dependence and instability of wind speed, a short-term wind speed prediction model based on VMD and deep GRU network is proposed in this paper. In order to reduce the instability of original sequence, VMD is used to obtain the limited number of sub-sequences from the original wind speed sequence. Deep GRU network is adopted to build the prediction model for each sub-sequence. The overall prediction is obtained by superposing each predicting sequence. The case study shows that the proposed model can effectively track the changes of wind speed, and has relatively high accuracy of short-term wind speed prediction.