挖掘电力负荷数据的潜在价值是电力行业的主要课题之一，针对直接聚类方法难以提取高维电力负荷数据的潜在特征的问题，提出一种基于一维卷积自编码器的负荷特征优化聚类方法，对典型负荷特征进行优化聚类。首先采用一维卷积自编码器以最优化重构损失为目标，提取用户日负荷曲线的时序特征，实现对数据的非线性降维。其次，提出一种改进聚类结构信息的cayley正交约束方法，对潜在空间内特征进行映射优化，提高聚类稳定性。然后采用GAN-Kmeans聚类方法优化聚类中心对编码部分进行微调，从而提高特征分类识别的准确度。最后，基于用户负荷数据集，采用DBI、CHI和SC三个内部指标进行有效性评估，结果表明所提方法能有效识别并提取各类负荷曲线的形态特征，可为虚拟电厂的需求响应和优化调度提供可靠支持。

Mining the potential value of power load data is one of the main topics in the electric power industry. Aiming at the problem that it is difficult to extract the potential features of high-dimensional power load data by direct clustering methods， a one-dimensional convolutional autoencoder-based load feature optimization clustering method is proposed to optimize the clustering of typical load features. Firstly， a one-dimensional convolutional autoencoder is used to extract the time-series features of the user's daily load profile with the objective of optimizing the reconstruction loss to achieve the nonlinear dimensionality reduction of the data. Second， a cayley orthogonal constraint method for improving the clustering structure information is proposed to optimize the mapping of the features in the potential space and improve the clustering stability. Then the GAN-Kmeans clustering method is used to optimize the clustering center to fine-tune the coding part， so as to improve the accuracy of feature classification and identification. Finally，based on the user load dataset， three internal indicators， DBI， CHI and SC， are used to evaluate the effectiveness of the proposed method， which shows that the proposed method can effectively identify and extract the morphological features of various load curves， and it can provide a reliable support for demand response and optimal scheduling of virtual power plants.