随着可再生能源机组的大规模并网，其出力波动性对电网的稳定运行构成了严峻挑战。为准确预测短期风电机组的输出功率提出了基于集合经验模态（ensemble empirical mode decomposition，EEMD）算法和考虑注意力（Attention）机制加强双向门控循环单元（bidirectional gated recurrent unit，BiGRU）的短期风电功率预测方法。首先，利用EEMD算法对现场传感器采集的弱平稳原始环境特征序列进行数据预处理，将非线性环境特征序列分解为多个固有模态和残差项，以降低随机扰动信号对组合模型预测精度的影响；然后，将Attention机制引入BiGRU网络，为相应的隐含层状态赋予权重系数，以深入地挖掘环境模态特征序列中的潜在信息，同时采用改进灰狼优化算法对已调整权重系数后的BiGRU-Attention网络超参数进行优化，建立了每个特征模态子序列的关联模型；最后，使用训练获得的EEMD-OBiGRU-Attention组合模型对分解后各层模态特征子序列进行多维时空预测，并重构模态特征预测的集合以生成结果输出。算例结果表明，与传统预测模型相比，EEMD-OBiGRU-Attention组合模型在现场短期风电功率预测中具有更高的鲁棒拟合精度。

As renewable energy units are connected to the grid on a large scale, the variability of their output poses a severe challenge to the stable operation of the power grid. In order to accurately predict the short-term output power of wind turbine units, a short-term wind power prediction method is proposed based on ensemble empirical mode decomposition （EEMD） algorithm and attention-enhanced bidirectional gated recurrent unit （BiGRU）. Firstly, the EEMD algorithm is used for data preprocessing of the non-stationary original environmental feature sequences collected by sensors on-site, decomposing the nonlinear environmental feature sequences into multiple intrinsic modes and residuals to reduce the impact of random disturbance signals on the training accuracy of the composite model. Then, the Attention mechanism is introduced into the BiGRU network, assigning weight coefficients to the corresponding hidden layer states to delve deeper into potential information within the environmental mode feature sequences. An improved grey wolf algorithm is then used to optimize the initial hyperparameters of the BiGRU-Attention network post weight coefficient adjustment, establishing association matrices for each environment mode subsequence. Finally, using the trained EEMD-OBiGRU-Attention combined model, multidimensional temporal and spatial predictions are performed on the decomposed modal feature subsequences at each layer, and the reconstructed set of modal feature predictions is used to generate output results. Example results show that, compared to traditional prediction models, the EEMD-OBiGRU-Attention combined model has higher robust fitting accuracy in on-site short-term wind power prediction.