在区域风电短期功率组合预测领域，常用的深度学习预测模型虽然能够充分学习各单一模型的预测特征，但样本数据规模较小时，深度学习模型容易对训练数据分布产生依赖，从而产生过拟合现象；此外，虽然聚类方法被用来提升区域级预测的精度，但现有方法的聚类目标通常为最小化不同类中风电场的差异，如地理位置差异，而未考虑与预测目标的一致性。为解决以上问题，本文提出一种基于闭环聚类和多目标优化的风电短期功率预测方法。在该方法中，首先通过闭环聚类算法将风电场分为多类；对于每类中的风电场数据，运用Bootstrap方法从原始数据集中随机有放回地抽取N组训练子集；然后利用N组子集数据分别训练卷积神经网络模型；最后采用多目标优化算法对N个卷积神经网络的预测结果进行集成。基于中国内蒙古地区的实际风电场数据开展算例验证，结果表明，所提方法在均方根误差方面相比长短期记忆网络模型降低了33.81%，相比基于卷积神经网络的组合预测模型降低了24.08%，相比基于K-means聚类方法的预测降低了14.05%。

In the field of regional short-term combined forecasting of wind power, although deep learning methods can effectively learn the predictive features of each individual model, they tend to result in overfitting when the sample data size is small. Additionally, although clustering methods are used to improve the accuracy of regional-level forecasting, existing methods typically aim to minimize the dissimilarity among wind farms, such as geographic dissimilarity, without considering consistency with the forecasting objectives, which involve minimizing forecasting errors. To address these issues, this paper proposes a short-term wind power prediction method based on closed-loop clustering and multi-objective optimization. First, wind farms are divided into multiple clusters through the closed-loop clustering method. For each cluster, the Bootstrap method is utilized to randomly extract N training subsets with replacement from the original dataset. Subsequently, N convolutional neural networks are trained independently using these subsets. Finally, multi-objective optimization is employed to integrate the prediction results from the N convolutional neural networks. Case studies utilizing wind farm data from Inner Mongolia, China, demonstrate that the proposed method reduced root mean square error by 33.81% compared to the long short-term memory model, by 24.08% compared to the convolutional neural network-based combined forecasting model, and by 14.05% compared to predictions based on the K-means clustering method.