随着“双碳”战略目标和新型电力系统建设的不断推进，传统配电网逐渐向信息化、数字化和智能化的新型配电系统转变。为准确刻画并分析配电网中不同类型负荷特性，支撑配电网高效运行管控，提出了一种基于数据驱动的配电网典型负荷曲线分类方法。首先基于负荷数据，分析了配电网典型负荷的多种分类场景，并提出了包括错误率、精度和混淆矩阵等的分类场景性能评价指标；在此基础上，提出了一种基于数据驱动的配电网负荷分类方法，将24维日负荷向量转换成图片数据，并基于卷积神经网络识别负荷曲线图片，实现对配电网负荷曲线的精准分类；最后结合实际配电网负荷数据对所提方法的准确性与有效性进行了验证，并与已有方法进行了分析与对比。结果表明所提配电网典型负荷曲线分类方法具有更好的分类速度和分类精度。

With the continuous promotion of the "dual carbon" strategic goals and the construction of new power systems, traditional distribution networks are gradually transforming into information-based, digital, and intelligent new distribution systems. To accurately characterize and analyze the characteristics of different types of loads in the distribution network, and support efficient operation and control of the distribution network, a data-driven classification method for typical load curves in the distribution network was proposed. Firstly, based on load data, various classification scenarios of typical loads in the distribution network were analyzed, and performance evaluation indicators for classification scenarios including error rate, accuracy, and confusion matrix were proposed. On this basis, a data-driven load classification method for distribution networks was proposed, which converts 24 dimensional daily load vectors into image data and uses convolutional neural networks to identify load curve images, achieving accurate classification of distribution network load curves. Finally, the accuracy and effectiveness of the proposed method were verified by combining actual distribution network load data, and analyzed and compared with existing methods. The results indicate that the proposed method for classifying typical load curves in power distribution networks has better classification speed and accuracy.