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Geselecteerde taal: EN

• Internship | Machine Learning for Smart Gas Flow Metering in Future Energy Systems

Locatie: Rijswijk Werken op afstand: Hybrid Opleidingsniveau: Master Uren per week: 32-40 hours/week Vacancy 1356

Internship | Machine Learning for Smart Gas Flow Metering in Future Energy Systems

Make your mark on our time. Become an intern at TNO!

About this position

The European Commission aims to achieve a carbon-neutral energy system by 2050, requiring a transition from natural gas to renewable energy gases such as biomethane and hydrogen. This transition introduces new challenges for gas transmission and distribution networks, including increased variability in gas composition, supply and demand, flow rates, and a growing number of grid entry points.

These developments place higher demands on gas flow measurement. Current fiscal metering practices are estimated to underestimate measurement uncertainty by approximately 35%, while accurately quantifying flow meter uncertainty remains essential for metrological traceability, gas allocation, and billing. Consequently, there is a growing need for reliable models that describe gas network dynamics using high-quality measurement data.

To address these challenges, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) are increasingly exploring Artificial Intelligence (AI) and Machine Learning (ML) to enhan...