Benchmark-grounded self-supervised contrastive learning for AMI-based distribution grid monitoring

Abstract

Self-supervised contrastive learning is effective for time-series representation learning, but its deployment in industrial environments remains challenging because of spatiotemporal heterogeneity, domain-specific noise, and label scarcity. We study these challenges in the smart-grid domain, a representative cyber-physical system characterized by non-stationary events, missing data, and policy-driven distribution shifts. We introduce the West China AMI Benchmark (WCA-Bench), a real-world dataset containing 713,700 user-day electricity profiles from 1,950 users and 506 expert-verified structural evolution events, including electric-vehicle access and photovoltaic installation. We further propose Domain-Aware Self-Supervised Contrastive Learning (DA-SSCL), which combines a lightweight PatchMLP encoder, physically grounded augmentations, and a hierarchical instance- and patch-level InfoNCE objective. DA-SSCL achieves an unsupervised clustering ARI of 0.699, reaches 88.4% classification accuracy with only 10% labeled data, and maintains transferability across the evaluated heterogeneous city districts. These results provide a practical methodology and a verified benchmark for robust, label-efficient, and transferable time-series representation learning in cyber-physical systems.