With grid operators confronting rising uncertainty from renewable integration and a broader push toward electrification, Demand-Side Management (DSM) -- particularly Demand Response (DR) -- has attracted significant attention as a cost-effective mechanism for balancing modern electricity systems. Unprecedented volumes of consumption data from a continuing global deployment of smart meters enable consumer segmentation based on real usage behaviours, promising to inform the design of more effective DSM and DR programs. However, existing clustering-based segmentation methods insufficiently reflect the behavioural diversity of consumers, often relying on rigid temporal alignment, and faltering in the presence of anomalies, missing data, or large-scale deployments. To address these challenges, we propose a novel two-stage clustering framework -- Clustered Representations Optimising Consumer Segmentation (CROCS). In the first stage, each consumer's daily load profiles are clustered independently to form a Representative Load Set (RLS), providing a compact summary of their typical diurnal consumption behaviours. In the second stage, consumers are clustered using the Weighted Sum of Minimum Distances (WSMD), a novel set-to-set measure that compares RLSs by accounting for both the prevalence and similarity of those behaviours. Finally, community detection on the WSMD-induced graph reveals higher-order prototypes that embody the shared diurnal behaviours defining consumer groups, enhancing the interpretability of the resulting clusters. Extensive experiments on both synthetic and real Australian smart meter datasets demonstrate that CROCS captures intra-consumer variability, uncovers both synchronous and asynchronous behavioural similarities, and remains robust to anomalies and missing data, while scaling efficiently through natural parallelisation. These results...

翻译：随着电网运营商面临可再生能源并网带来的日益增长的不确定性，以及电气化进程的广泛推进，需求侧管理（DSM）——特别是需求响应（DR）——作为一种平衡现代电力系统的经济有效机制，已引起广泛关注。全球范围内持续部署的智能电表产生了前所未有的海量用电数据，使得基于实际使用行为的消费者分群成为可能，这有望为设计更有效的DSM与DR方案提供依据。然而，现有的基于聚类的分群方法未能充分反映消费者的行为多样性，通常依赖于僵化的时间对齐方式，且在存在异常值、缺失数据或大规模部署时表现不佳。为应对这些挑战，我们提出了一种新颖的两阶段聚类框架——优化消费者分群的聚类表征（CROCS）。在第一阶段，对每位消费者的日负荷曲线进行独立聚类，形成代表性负荷集合（RLS），从而对其典型的日用电行为进行紧凑概括。在第二阶段，采用加权最小距离和（WSMD）这一新颖的集合间度量方法对消费者进行聚类，该方法通过同时考量行为模式的普遍性与相似性来比较不同RLS。最后，在WSMD导出的图上进行社区检测，可揭示体现消费者群体共同日用电行为的高阶原型，从而增强所得聚类结果的可解释性。在合成数据集与真实澳大利亚智能电表数据集上的大量实验表明，CROCS能够捕捉消费者内部用电模式的变异性，发现同步与异步的行为相似性，对异常值和缺失数据保持鲁棒性，并通过天然并行化实现高效扩展。这些结果...