SmartGasNet project was presented last May 15 to the Working Group on Fluid Flow (WGFF) of the Consultative Committee for Mass and Related Quantities (CCM) at the 2026 CCM-WGFF Meeting in Tsukuba, Japan. The CCM is a technical committee under the Bureau International des Poids et Mesures (BIPM), which ensures a globally coherent system of measurements traceable to the International System of Units (SI).
During the session, Gregor Bobovnik, from the University of Ljubljana, presented SmartGasNet, outlining its main research directions. These include the development of experimental datasets for time-resolved gas flow measurements under controlled and realistic conditions, the application of advanced time-series analysis methods for characterising dynamic behaviour in gas systems, and the use of machine learning techniques to predict gas flow monitoring.
This engagement with the CCM-WGFF represents an important step in strengthening SmartGasNet’s collaboration with high-level metrology stakeholders. The WGFF plays a central role in supporting National Metrology Institutes (NMIs) and partner organisations in the development, validation, and maintenance of a coherent global measurement system for flow and related quantities. Furthermore, this working group operates under the auspices of the BIPM, a leading organisation in the establishment of measurement systems traceable to the International System of Units (SI). Within this framework, SmartGasNet contributes to ongoing discussions on how data-driven approaches can complement and enhance established metrological practices to upscale renewable gases such as biomethane and hydrogen.
Looking ahead, the project is preparing a stakeholder discussion focused on the evaluation of machine learning models in gas grid monitoring. We welcome feedback from experts who are interested in contributing to this discussion and shaping future evaluation frameworks for machine learning in gas systems. If you are interested, click on the following link: https://smartgasnet.eu/contact/
