# CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data

arXiv: https://arxiv.org/abs/2601.10494 · HTML: https://mlsanity.com/paper/2601.10494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 120 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 14 of 14)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [Are Cluster Validity Measures (In)valid?](https://mlsanity.com/paper/2208.01261/) (2022)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Shape-Based Approach to Household Load Curve Clustering and Prediction](https://mlsanity.com/paper/1702.01414/) (2017)
- [A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs](https://mlsanity.com/paper/2310.20367/) (2023)
- [Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances](https://mlsanity.com/paper/1911.03336/) (2019)
- [Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day?](https://mlsanity.com/paper/1409.1043/) (2014)
- [Dynamic Time Warping Clustering to Discover Socio-Economic Characteristics in Smart Water Meter Data](https://mlsanity.com/paper/2112.13778/) (2021)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids](https://mlsanity.com/paper/2106.05905/) (2021)
- [Community Detection in Directed Weighted Networks using Voronoi Partitioning](https://mlsanity.com/paper/2304.12389/) (2023)

## Abstract (arXiv)

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...