Sensors are set to be installed across the station to capture real-time data on the station’s energy use. This approach to understanding and improving the station’s energy performance is thanks to a computer modelling platform that has developed a digital twin of the station, including a simulation of its current energy usage.
Using existing, historical data and modelling, opportunities have been identified that are predicted could result in around a 20% improvement in energy performance.
The digital twin was created by consulting engineer Atkins, using Cardiff University’s Computational Urban Sustainability Platform (CUSP).
Using this data and computer modelling to create baselines for the station’s energy consumption and carbon emissions, CUSP has been used to map out ways of improving the station’s performance via energy efficiency measures while exploring further possible changes and the potential impact this could have on making additional energy savings.
Energy saving ideas that all this technology has come up so far are:
- turning off escalators and other machinery when not in use or overnight when the station is shut.
- automatically dimming lights in areas not in use.
In addition to the data being collected by the sensors at the station, passenger numbers and research to understand passenger and station-user behaviour will also be recorded to understand how identified energy savings might impact their safety and experiences when in the station.
It is hoped that the Reading station initiative will be rolled out across other stations.
Nick Tune, technical & technology director at Atkins, said: “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.”