Background

The University of Wales Trinity St David (UWTSD) has made impressive strides in reducing its operational carbon emissions, energy consumption, and associated costs across its estate. This achievement is largely thanks to the innovative use of Optimise AI’s semantic digital twin technology, which helps the university understand how and when its buildings use energy.

UWTSD’s estate is a mix of modern, state-of-the-art facilities and charming pre-1900s structures. Managing energy consumption across such a diverse range of buildings is no small feat, especially without comprehensive performance data. This lack of clarity made effective energy analysis and optimization quite challenging.

The Challenge

UWTSD’s primary challenge in its journey towards decarbonisation was the absence of real-time performance data across its buildings. Without accurate insights into energy consumption patterns, it was nearly impossible to identify inefficiencies and implement effective reduction strategies.

The Solution

To tackle this data gap, UWTSD turned to OptimiseAI’s semantic digital twin technology. By collecting data from smart meters, sensors, and other building management systems (BMS), the AI-powered platform fills in missing data gaps using advanced machine learning algorithms. It then analyzes this information to generate actionable insights and optimization scenarios, which can be implemented manually by the facilities management team or automatically via the BMS.

UWTSD adopted two of OptimiseAI’s products:

• Predict: This tool analyzes energy consumption at an estate-wide level, particularly in older, data-poor buildings that lack detailed sensor coverage.

• Optimise: This tool is applied to newer, data-rich buildings, providing granular real-time insights and AI-driven recommendations for efficiency improvements.

With these tools, the university can now monitor building performance in near real-time and implement targeted energy-saving measures. Asset managers can track improvements and measure benefits live, ensuring an adaptive and responsive approach to decarbonisation.

Results and Impact

Since implementing these solutions, UWTSD has gained visibility into its energy usage and efficiency. The university is on track to achieve:

• 20% energy savings in older, data-poor buildings using Predict.

• 30% energy savings in newer, data-rich buildings using Optimise.

Looking ahead, the university plans to enable OptimiseAI’s algorithms to autonomously control the BMS, ensuring optimal occupant comfort while minimizing energy waste and carbon output.

"I am blown away by the insights a semantic digital twin can provide with limited data. This is supporting us to plan our decarbonisation programme, but most importantly engage with our students and staff to implement improvements and track benefits."

Kelly Williams, Executive Head of Operational Estates and Facilities at UWTSD,

Conclusion

UWTSD’s partnership with OptimiseAI is a positive example of how AI and digital twins can drive meaningful sustainability and operational efficiency improvements. By harnessing the power of digital twins, the university is paving the way for a more data-driven and sustainable future, ensuring it meets its net-zero ambitions while optimizing operational performance.