为了提升风电功率概率区间预报性能，提出了一种基于Critic权重法与反熵权法（anti-entropy weight method）的变带宽混合滑动Gaussian核密度估计（variable bandwidth hybrid sliding Gaussian kernel density estimation，VHSKDE（Gaussian））与正态滑动指数迭代估计（normal sliding exponential iteration，NSEI）组合的风电功率区间概率预报方法。该组合方法简称为VHSKDE（Gaussian）-NSEI。首先，通过基于变分模态分解与长短期记忆神经网络（variational mode decomposition-long short-term memory，VMD-LSTM）点预报得到偏差。然后，分别利用VHSKDE（Gaussian）和NSEI估计预报偏差的概率分布，得出对应的置信概率下的预报区间。最后，利用4种客观权重赋值法分别对VHSKDE（Gaussian）环节及VHSKDE（Gaussian）-NSEI组合环节进行两次加权组合生成最终的风电功率预报区间。研究结果表明，VHSKDE（Gaussian）-NSEI预报模型在不同置信度情况下能够兼顾PICP与PIAW的优良性能，与NSEI和VHSKDE（Gaussian）相比具有更高的可靠性和准确性，为风电功率概率预报提供了重要参考。

In order to improve the performance of wind power probability interval prediction, a wind power interval probability prediction method is proposed which is the combination of variable bandwidth hybrid sliding Gaussian kernel density estimation (VHSKDE(Gaussian)) and normal sliding exponential iteration (NSEI) based on critical weight method and anti-entropy weight method. The combination method is called VHSKDE(Gaussian)-NSEI for short. Firstly, the error is obtained by point prediction based on variational mode decomposition and long short-term memory neural network(VMD-LSTM). Then, VHSKDE(Gaussian) and NSEI are used to estimate the probability distribution of the prediction errors, and forecast interval is obtained under corresponding confidence probability. Finally, four objective weight assignment methods are used to weight the VHSKDE(Gaussian) link and the VHSKDE(Gaussian)-NSEI combination link twice to generate the final wind power prediction interval. The research results show that the excellent performances of PICP and PIAW can be compatible by using the proposed VHSKDE(Gaussian)-NSEI prediction model under different levels of confidence. The VHSKDE(Gaussian)-NSEI prediction model has higher reliability and accuracy than NSEI and VHSKDE(Gaussian), and provides an important reference for wind power probability prediction.