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。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，采用生成对抗网络(generative adversarial network, GAN)结合 K-means聚类方法优化聚类中心对编码部分进行微调。最后，基于用户负荷数据集，采用戴维斯-博尔丁指数(Davies-Bouldin index, DBI)、卡林斯基-哈拉巴斯指数(Calinski-Harabasz index, CHI)和轮廓系数(silhouette coefficient, SC)这3个指标进行有效性评估。结果表明所提方法在提升聚类结果簇间区分度和簇内紧密度方面的显著优势。研究表明该方法可以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。

Extracting the latent value embedded in electricity load data constitutes one of the key challenges in the power industry. To address the difficulty faced by conventional clustering approaches in capturing the intrinsic features of high-dimensional load data, this paper proposes an optimized clustering method based on a one-dimensional convolutional autoencoder (1D-CAE). First, a 1D-CAE is employed to extract temporal features from daily customer load profiles through nonlinear dimensionality reduction, with the objective of minimizing reconstruction loss. Second, we introduce an improved Cayley orthogonal constraint to enhance the structural information of the clustering space, thereby optimizing the mapping of latent features and improving clustering stability. Third, a generative adversarial network (GAN) is integrated with K-means clustering to refine the cluster centers and fine-tune the encoder. Finally, the effectiveness of the proposed method is evaluated on real-world load datasets using three widely accepted internal validation metrics: the Davies–Bouldin index (DBI), the Calinski–Harabasz index (CHI), and the silhouette coefficient (SC). Experimental results demonstrate that the proposed approach significantly enhances both inter-cluster separability and intra-cluster compactness. The study confirms that the method can effectively identify and extract morphological characteristics of diverse load profiles, offering robust support for demand response and optimal dispatch in virtual power plants.