Introduction

ScotRail has set an ambitious goal of achieving net-zero emissions by 2045. Significant progress has been made by running much of its electric train fleet on renewable energy. However, reducing the energy usage of stations and depots across Scotland remains a challenge.

Stations are often unique, historic, and have high energy demands, being open to the public for large parts of the day. Managing energy consumption across a diverse range of stations and depots, from Aberdeen to Clydebank, is a complex task.

The Challenge

ScotRail needed to effectively understand, manage and reduce energy consumption across its extensive network of stations and depots.

Traditional methods often involve sporadic one off reporting or attempting to analyse siloed data. This provided limited insights, making it difficult to implement efficient energy-saving strategies.

The Solution

By partnering with Optimise-AI, ScotRail initially embarked on a journey to test the potential impact of digital twins.

Many railway facilities have data but lack the ability to bring this together to automate and optimise energy use. For instance, ticket gate data could help adjust lighting based on real-time passenger flow, improving efficiency and comfort.

With a centralised view of energy use across the rail estate, it’s easier to spot inefficiencies and direct investment opportunities effectively.

ScotRail found that with just a few inputs, they could immediately understand their actual energy performance, initially across a small number of test stations using Predict.

Following this initial trial, the partnership was rapidly expanded to cover 25 stations and depots, with advanced optimisation at eight major sites including Aberdeen, Inverness and Queen Street.

In these eight 'complex' stations, energy needs vary widely between zones. Without smart systems, it's challenging to use energy in response to factors like weather, occupancy, train schedules, ticket barrier data or events. By using Optimise, it allowed ScotRail to bring multiple data sources together, adding further granularity in planning energy usage across stations, zones, platforms and rooms.

How is this possible? The team at OptimiseAI rely on a decade of research data from other buildings, combined with advanced AI technology such as semantics, to create digital twins based on many disparate data sets. These digital twins provide real-time tracking and optimisation features, allowing ScotRail to monitor energy consumption at every level—from entire buildings to individual rooms and systems.

Implementation

The simple dashboard allowed ScotRail to use the platform with little guidance. Immediately ScotRail could benefit from energy recommendations and adjustments, focusing decarbonisation efforts and investment priorities where they’re needed most.

The technology sits on top of existing technology stacks and identifies disparate data sets, bringing data together with minimal integration needs.

The system's real-time tracking capabilities enable ScotRail to test decarbonisation strategies within the digital twin, demonstrate ROI, and continuously track and optimise savings. Investment decisions can be based on real data with the payback acurately tracked.

Results

The potential savings are substantial, with reductions projected of up to 40% in the short to medium term. Poul Wend Hanson, ScotRail’s Head of Sustainability, shared his enthusiasm for the project, stating:

“Reducing our carbon footprint and improving energy efficiency is a top priority for ScotRail.

By expanding our partnership with OptimiseAI with this further rollout of digital twins, we can gain a deeper understanding of our energy use and take meaningful steps to reduce emissions across all our stations and depots. It will help us build a greener future for Scotland’s Railway.”

As Optimise-AI’s system gathers more data and refines its models, it is anticipated that ScotRail will achieve even greater insights, resulting in further reductions in both carbon emissions and costs.

Nick Tune, CEO at OptimiseAI outlined a little more on the progress.

"We're proud to be working with ScotRail on their plans to reduce their station and depot energy emissions. By expanding the use of digital twins ScotRail is deploying an intelligent energy management system that will reduce energy costs across its buildings. This kind of scalable approach represents the future we're heading toward: where efficient building estates will be the backbone of resilient cities."

Conclusion

This partnership represents a significant opportunity not only for ScotRail but for the entire rail industry, showcasing the financial and environmental advantages of AI-driven solutions. By embracing cutting-edge technology, ScotRail is setting a precedent for sustainable practices and leading the way toward a greener future.