Energy – AI-Powered Anomaly Detection for Smart Energy Meter Data

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.

Client context

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.

Business challenges

• Improve anomaly detection without extensive manual business-rule coding
• Process heterogeneous data from single-phase and three-phase meters at high reliability
• Reduce the operational workload of error qualification and analysis
• Scale detection performance while simplifying operational monitoring

The T&S solution

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.

Technologies & expertise

• Artificial Intelligence
• Data quality management
• Smart-grid analytics
• Anomaly detection
• Energy data processing
• Industrial data integration

Results & business value

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.

Conclusion

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.