Enabling Privacy-Preserving Natural Language Access to Industrial Smart Meter Data

Researchers posted arXiv:2611.09372v1, describing OpenMeter, a schema-agnostic multi-agent system that translates natural language queries over industrial smart meter telemetry into executable code and human-readable insights so non-technical utility staff can query databases without SQL expertise and without routing sensitive consumer data through cloud-based LLMs. Multi-scenario ablation studies found full multi-agent orchestration delivered its largest accuracy gain on resource-constrained local hardware, at +6.1 percentage points execution accuracy (EX-R) over the strongest single-agent configuration, while at frontier scale individual components retained value but should be enabled selectively, indicating full orchestration's cost is justified specifically where privacy demands local execution.

arXiv:2611.09372v1 Announce Type: cross Abstract: Energy management officers and grid operators increasingly rely on smart meter data for anomaly detection, performance monitoring, and strategic decision-making. However, extracting actionable insights from large-scale, heterogeneous telemetry databases requires advanced SQL and data engineering expertise, creating a critical bottleneck for non-technical personnel. Furthermore, the strict data privacy regulations and security constraints inherent to critical energy infrastructure often prohibit the routing of sensitive consumer data through cloud-based Large Language Models LLMs . To address these challenges, we present OpenMeter, a schema-agnostic Multi-Agent System MAS designed to translate natural language queries into executable code and human-readable insights. This architecture lowers the technical barrier for day-to-day utility operations by empowering non-expert personnel to interact with complex databases through intuitive natural language dialogue, transforming raw telemetry into a highly valuable tool for routine grid workflows and strategic decisions. Multi-scenario ablation studies over this environment demonstrate that full multi-agent orchestration shows its largest observed accuracy gains on resource-constrained local hardware +6.1 pp EX-R over the strongest single-agent configuration ; at frontier scale, individual components retain value but should be enabled selectively, indicating that the cost of full orchestration is justified specifically where privacy demands local execution.