T&S deployed an AI-driven data-quality solution detecting anomalies across millions of daily smart-meter measurements for a European energy operator.
As smart-grid infrastructures expand, utility operators must manage growing volumes of meter data while ensuring reliability and accuracy. A major European energy distribution operator partnered with T&S to deploy an AI-driven data-quality solution automatically detecting anomalies across millions of daily measurements, improving error detection for single-phase and three-phase smart meters while cutting manual qualification effort.
The client, a leading energy distribution company managing a nationwide smart-meter network, needed to strengthen monitoring and qualification of data from connected electricity meters. Rule-based approaches required heavy manual effort and adapted poorly to evolving usage and subscription models, so the client sought a scalable, intelligent approach detecting anomalies directly from daily meter streams.
T&S deployed and configured an AI-powered data-quality solution for smart-meter anomaly detection and qualification. It identifies abnormal behaviour directly from daily meter data without extensive predefined business rules, detecting incoherent consumption-index evolution, abnormal maximum power values, inconsistent distributor/supplier readings and irregular phase-level power behaviours on three-phase installations. T&S also supported integration and configuration to ensure compatibility with the client's operational environment.
Higher detection quality with reduced manual analysis and qualification workload. Automated handling of usage conditions and subscription rules simplified operational monitoring and reinforced the reliability of smart-meter data, supporting more accurate operational decisions.
The project highlights T&S's expertise in combining AI, data analytics and energy-infrastructure knowledge to solve large-scale operational challenges and support the digital transformation of smart-grid operations.
