基于贝叶斯优化的VMD-GRU短期风电功率预测

为提高风电功率预测精度，提出了一种基于贝叶斯优化的变分模态分解(variationalmodedecomposition,VMD)和门控循环单元(gatedrecurrentunit, GRU)相结合的风电功率预测方法。首先使用VMD算法对风电功率序列进行分解，并根据排列熵(permutation entropy, PE)的大小来确定序列分解的最佳模态数。然后将分解后得到的子序列分量与关键气象变量数据结合构成模型输入特征。使用GRU网络对各个子序列分量分别进行预测，并将各个子序列分量的预测结果进行重构得到风电功率预测结果。最后采用贝叶斯优化方法对各个子序列预测模型的网络初始超参数进行优化。采用某风电场的风电数据对所提模型进行验证，并与其他6种模型进行性能对比。结果表明，基于贝叶斯优化的VMD-GRU预测模型明显优于其他模型，具有较好的泛化能力，能够有效提高风电功率预测精度。

Short-term wind power prediction of a VMD-GRU based on Bayesian optimization

To improve the accuracy of wind power prediction, a wind power prediction method based on Bayesian optimization variational mode decomposition (VMD) and a gated recurrent unit (GRU) is proposed. First, the VMD algorithm is used to decompose the wind power sequence, and the optimal mode number of sequence decomposition is determined according to the size of permutation entropy (PE). Then, the decomposed sub-sequence components are combined with the key meteorological variable data to form the input characteristics of the model. The GRU network is used to predict each sub-sequence component separately, and the prediction results of each sub-sequence component are reconstructed to obtain the wind power prediction results. Finally, the Bayesian optimization method is used to optimize the network initial hyperparameters of each subsequent prediction model. The proposed model is evaluated on a real wind power data from a wind farm and compared with six baseline models. The results show that the VMD-GRU prediction model based on Bayesian optimization is clearly the superior model, has better generalizability, and can effectively improve the prediction accuracy of wind power.