Predictive Tool for Unaccounted-For Gas (UAG) Identification

Predictive Tool for Unaccounted-For Gas (UAG) Identification

Status: Live
Reference: NIA_NGT0277
Lead Network: National Gas Transmission PLC
Start Date: 01 August 2025
End Date: 01 February 2026
Budget: £210,000
Funding Mechanism: Network Innovation Allowance
Technology: Active Network Management, Condition Monitoring, Digital Network, Gas Transmission Networks, Maintenance Inspections, Measurement, Network Monitoring, Resilience
Strategy Theme: Net zero and the energy system transition
Collaborating Networks: National Gas Transmission PLC
Project Partners: The are the sole project partners for this project.

Abstract

The Unaccounted-for Gas (UAG) project aims to develop a predictive tool that identifies and quantifies UAG across the National Transmission System (NTS). Leveraging 12-18 months of SCADA data, the tool will simulate gas flow and metering behaviour to pinpoint anomalies and reduce losses. UAG currently represents significant financial cost to the consumer; even a 1% reduction could yield practical savings. The project aligns with RIIO-2 NIA criteria and supports regulatory compliance under Special Condition 5.6. It builds on prior research, and integrates learnings from international benchmarks. The initiative will enhance operational efficiency, improve data transparency, and support long-term decarbonisation goals through better system visibility and control.

The project is expected to generate significant new learning in the areas of gas flow modelling, metering behaviour, and the root causes of Unaccounted-for Gas (UAG) across the National Transmission System (NTS). Specifically, the project will enhance understanding of how telemetry data can be used to simulate system behaviour and identify anomalies at both system-wide and site-specific levels.

Key learning outcomes will include:

Techniques for cleansing and structuring SCADA data for predictive modelling.
Insights into the relationship between metering bias and UAG patterns.
Validation of machine learning approaches for operational gas network analysis.
Recommendations for future integration of predictive tools into business-as-usual processes.

Learning will be disseminated through a final project report, internal briefings, and submission to the ENA Smarter Networks Portal. Where appropriate, findings will also be shared with Ofgem and other industry stakeholders to support wider adoption and regulatory alignment.

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