基于SSA-VMD-LSTM-NKDE的短期风电功率概率预测

为进一步提高风电功率预测精度，提出一种基于麻雀搜索算法(SSA)优化VMD参数的组合预测方法。首先，使用麻雀搜索算法对VMD参数进行优化，并利用优化后的VMD对数据进行分解；其次，结合灰色关联分析法和熵权法对环境变量进行相关性分析，选择相关性最高的影响因素与分解得到的各模态分量组合作为LSTM预测模型的输入，获得更为精确的预测结果；最后，建立基于非参数核密度估计(NKDE)的风电功率概率预测模型，实现对风电功率预测结果不确定性的有效量化。结果表明，所提组合模型的MAE，RMSE和MAPE比VMD-LSTM模型的分别下降了39.51%，33.22%和40.39%。SSA-VMD-LSTM-NKDE组合模型不仅能够有效提高确定性预测的精度，而且还能够实现对风电功率预测结果不确定性的有效量化，为风电功率预测提供了科学决策依据。

Short-term wind power probabilistic forecasting based on SSA-VMD-LSTM-NKDE

In order to further improve the accuracy of wind power forecasting, a combined forecasting method based on sparrow search algorithm (SSA) optimizing variational mode decomposition (VMD) parameters was proposed. Firstly, the SSA was used to optimize the VMD parameters, then the optimized VMD was used to decompose the data. Secondly, the entropy weight method and grey relational analysis were combined to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components were selected as the input of the LSTM prediction model to obtain more accurate prediction results. Finally, a wind power probability prediction model based on NKDE was established to effectively quantify the uncertainty of wind power prediction results. The results show that the MAE, RMSE and MAPE of the proposed combination model decrease by 3951%, 3322% and 4039%, respectively, compared with the VMD-LSTM model. The SSA-VMD-LSTM-NKDE combination model can not only effectively improve the accuracy of deterministic prediction, but also effectively quantify the uncertainty of wind power prediction results, which provides scientific decision-making basis for wind power prediction.