Digital twin boosts railway station’s energy performance

The Berkshire station is one of the busiest rail hubs in Britain, used by nearly 20 million passengers a year, of whom nearly four million change trains there.

Sensors are set to be installed across the site to capture live, real-time data on energy use, which will be fed into a ‘digital twin’ of the station that has been developed on a specialist computer-modelling platform. Using historical data and modelling, a number of opportunities have been identified that are predicted could result in around a 20 per cent improvement on the station’s carbon emissions and energy performance.

Network Rail has teamed up with engineering and design consultancy Atkins, and Cardiff University to develop a ‘digital twin’ of Reading station, utilising Cardiff University’s Computational Urban Sustainability Platform (CUSP).

Using the sensor data and computer modelling to create baselines, CUSP has been employed to map out ways of improving the station’s performance through energy efficiency measures and to explore the impact of further possible changes.

Low-cost solutions that have been identified include improved lighting controls such as dimming when an area of the station is not in use, and turning off machinery such as escalators when not in use or overnight.

In addition to the data being collected by the sensors at the station, passenger numbers and research into passenger and station-user behaviour will also be recorded to understand how identified energy savings might impact their safety and experience.

Adam De Benedictis, Network Rail’s regional energy and carbon manager, said: “We’re delighted to be working with Atkins on this innovative project which will help us gain a better understanding of complex assets such as Reading station and their predicted performance, allowing us to confidently identify and deliver energy efficiency measures and ultimately manage our assets effectively.”

Nick Tune, technical and technology director at Atkins, added: “This is an important milestone as we look to harness data and technology to improve delivery at every stage of an asset’s life. Digital twins are the centrepiece of this shift which is giving us the information needed to not only identify opportunities to improve an asset’s energy performance but to interrogate future scenarios, explore further recommendations and tell us how those interventions will work with an unprecedented degree of certainty.”