The project aims to transform how NESO identifies, investigates and prioritises faulty operational metering across the GB National Electricity Transmission System. Current metering quality management relies heavily on manual processes and engineering judgement, limiting the ability to identify hidden, emerging and system-wide faults and understand their operational impact. The project will investigate the use of Artificial Intelligence (AI), combining operational metering data, system knowledge and advanced analytics to develop, test and demonstrate new approaches for fault identification, impact assessment and explainable reporting. Outputs include benchmarking current practices, fault identification models, a prototype visualisation and reporting capability, and recommendations for future implementation. Learning will strengthen metering quality management, improve confidence in operational data, and enhance situational awareness, state estimation and network operation.
Benefits
The project aims to deliver a validated proof-of-concept capability for identifying, assessing and prioritising faulty operational metering using AI-enabled analytical techniques, together with evidence, benchmarking and recommendations to support future deployment.
Benefit 1: Knowledge (Direct Project Benefit)
Outcome Statement
AIM-FIX aims to develop and validate new methods for identifying, assessing and prioritising faulty operational metering, providing NESO with a better understanding of metering quality issues and their impact on system operation.
Who Benefits and How
NESO operational, engineering and analytical teams will benefit from improved visibility of metering quality issues, evidence-based prioritisation approaches, and a clearer understanding of where current processes can be enhanced. The wider electricity industry will also benefit through the sharing of project learning, methodologies and findings.
Confidence
High. The project will, if successful, directly deliver research outputs, analytical models, benchmarking studies, prototype tools and technical recommendations.
Benefit 2: Financial (Future Implementation Benefit)
Outcome Statement
If successfully deployed, the project could reduce the operational impact of faulty metering by enabling earlier identification and prioritisation of issues, supporting more efficient system operation and decision-making. Early error detection avoids inefficient balancing actions. Minimising metering errors reduces both under‑ and over‑procurement of response and reserve.
Who Benefits and How
NESO through reduced manual investigation effort and improved operational efficiency.
Electricity consumers through the potential reduction of avoidable system costs associated with poor-quality operational data.
Network operators and market participants through improved confidence in system monitoring and analysis.
Confidence
Medium. Realisation of financial benefits depends on successful future implementation and integration beyond the scope of this project.
NESO will gain improved understanding of metering quality issues, a benchmark of current metering performance, and new analytical methods for fault identification and prioritisation.
Benefit 3: System Resilience (Social Benefit)
Outcome Statement
The project aims to improve understanding of how metering quality affects system monitoring and operational awareness, supporting future improvements in network resilience and security of supply.
Who Benefits and How
NESO and the wider electricity system will benefit from greater confidence in operational data used to support system monitoring, state estimation, forecasting and operational decision-making.
Confidence
Medium to High, as the project will provide evidence of the relationship between metering quality and operational performance, although full resilience benefits depend on future deployment.
