The Role of Performance Digital Twins in Achieving Net-Zero Campus Estates - IES

Editor's Notes

• #3: Complex buildings ages and types Reduce financial risk
• #4: Digital technology is a key enabler. However, often buildings have multiple systems and data sources that whilst provide valuable information individually, only truly realise their potential when considered together Significant reductions in operational energy use are possible through optimisation of building systems, and analysis of the in-use phase of the building's lifecycle Understanding your retrofit plan in the longer term can help prioritise lower cost interventions that provide higher levels of benefit, operational drift for instance. Capital projects can be plotted against return on investment planning, ensuring a sustainable approach to decarbonisation pathways Current and emerging technologies also present opportunities to reduce the carbon impact of facilities while balancing other priorities, e.g. Optimise educational environment for staff & students Support occupant health & wellbeing Keep operational costs at a minimum Tracking performance
• #5: This slide is taken from an External source, the UKGBC – UK Green Building Council What this shows, is that with Optimisation alone, the average reduction in energy use intensity is 26% Light Retrofit along with Optimisation the average reduction is 37% And Deep Retrofit alongside Optimisation can achieve a 60-65% average reduction Clearly the greater the level of intervention, then the greater the impact on energy reduction, as this slide shows However, greater levels of intervention also generally have higher levels of cost to execute. Therefore understanding the desired impact of these interventions is critical in de-risking capital investment. One way of ensuring the impact of any of these, is through the use of digital twins and associated technology
• #6: Net Zero Opportunities -  Performance gap Drift even on well commissioned buildings-moving target, use etc. evolving-take occupancy as e.g. HVAC performance degrades, we can use that to look at renewable technology and more energy saving replacements Equipment breaks or becomes faulty,  do we just replace like for like, or can we use this to our advantage, and replace with more energy efficient alternatives? More often than not, we just replace like for like because it’s easy, or we don’t have control because someone else is in charge of replacing. Was the like for like replacement the right choice? Was that equipment even the right size or performing correctly?
• #7: Cost range – approx. £15-£30k dependant upon complexity. There currently exists many different interpretations of what a Digital Twin actually is, from purely a 3D model to an operational data model. A Performance Digital Twin/Building Performance Model is not just a nice 3D model or a data collected from sensors, it is an actual virtual replica of the building in use, based on physics simulations and improved with actual measured data. A Digital Twin behaves like the real counterpart to: Analyse how your buildings are performing now, how they should be performing, and explore different scenarios to understand the impacts of future changes Provide decision support information, to improve asset performance, influence future building design and retrofit and reduce investment risks Track performance
• #8: Performance Based Models… IES Digital Twins use both Physics enabled simulation, based on fundamental physics principles, and real-time data, which can be taken from electrical, gas and water utility bills, metering data, IoT sensors and BMS data and so on. Resulting in a real world Building Performance Model, a Digital Twin of your assets.​
• #9: Once we have created our digital twin, there are various use cases for which it can be deployed, as evidenced in this diagram.
• #10: A best practice approach to decarbonising buildings… Pretty straightforward but let’s go through the steps: Assess – First we need to assess..... Plan – We then need to create our plans Implement our plans...... Finally Monitor & Optimise – Ensure the value of your investments were delivered
• #11: So the next few slide will demonstrate the steps taken from initially creating a model, and what you can expect from the model Step 1: Building an Energy Model, which provides a virtual testbed for simulating retrofit scenarios. These lifelong digital assets can be leveraged for ongoing performance optimisation and future projects.  So depending on where you are on your journey; Baseline Energy Modelling ​(iCD) Is a fast and scalable method to create a robust baseline understanding of the current energy and carbon performance of a building. ​ Advanced Energy Modelling ​(VE+ Apache) Is a high detail energy model producing an accurate breakdown of energy use across the building. ​
• #12: Step 2: Once you have your model, we can start to look at Retrofit Scenario Testing  Leveraging the dynamic energy simulation models, Energy Conservation Measures and wider climate transition scenarios can be tested, such as:​ Weather and climate scenarios (e.g. 2050 forecast)​ Operational changes (e.g. Heating & Cooling Setpoints)​ Occupancy changes (e.g. increased occupancy density)​ Fabric Upgrades (e.g. replacement glazing)​ Technology replacements (e.g. Lighting replaced with LEDs)​ Conditioning Upgrades (e.g. Boilers replaced with ASHPs)​ Adding Renewables (e.g. Installation of PV, EV Charging)​​
• #13: Step 3: Decarbonisation pathways This is where additional analysis can be conducted informing decision making: And brings together all the selected improvement scenarios to create a decarbonisation roadmap or pathway, and plot the impact of these changes over your chosen time, whether that be 2030, 2040 or 2050 With everything in one easy to share place to compare impacts andtrack your potential journey. 
• #14: Ongoing Energy Management  So once you have created your Digital Twin, you have implemented the chosen measure or measures does the digital twin it stop there?..... No! It's important to continue to develop and take advantage of your digital twin asset for the ongoing lifecycle management of the building, to ensure you are avoiding common issues, e.g. High energy design standards achieved at design are not always achieved during operation – performance gap Plant sizing issues The changing nature of buildings and the fact they involve people means operation drifts But in fact all buildings “drift” even those perfectly commissioned and tuned Users change settings HVAC performance degrades Equipment breaks or becomes faulty Energy models used at design but not in operation become a wasted digital asset
• #15: So as we can see, a digital twin provides a lifetime asset which can be utilised across the entire lifecycle of your building or campus estate. Here we present an iterative, performance-based approach to Net-Zero investments – ongoing feedback loop support by the model. Net-Zero & Operational Energy Modelling at Design​ Effective Commissioning​ IES Live Set-Up​ Continued In Use Performance Evaluation​ Make the Business Case for Improvements​ Re-Commissioning after Retrofit Improvements​ Measurement and Verification of Retrofit Performance​ Continued In Use Performance Evaluation​ Keep Optimised Buildings on track​ Identify next round of Improvement Strategies​
• #17: We’ve been working with the University of Glasgow since 2015, first through the academics using licences to have their students create IES energy models of their building across the campus. Then we collaborated on the R&D project, eDigit2Life in 2019, using digital twins to help close the performance gap and optimise the whole-life performance of some of the university’s most intensely used student buildings. In 2023, the University appointed IES to continue the work from the previous R&D project, leveraging their digital twin technology to achieve three core goals: - Ensure new buildings on the campus perform as expected - Analyse and optimise existing buildings to help identify areas for improvement Look at the wider campus to define the University’s net-zero strategy, including decarbonisation of the district heating network For the commercial engagement with the University, a total of 5 performance digital twins were created. These included four new buildings – the James McCune Smith building (JMC), the Advanced Research Centre (ARC), the Institute of Health & Wellbeing (IHW) and the Adam Smith Building – and one existing building - the University Library. Two additional Digital Twins were also created at a campus level, enabling high level visualisation and analysis of net-zero strategies across the entire Gilmorehill campus. This also included a digital twin of the district heat network to identify solutions to reduce the carbon emissions of the University’s energy centre.
• #18: New buildings discovered not to beperforming as anticipated and using significantly more energy than budgeted – a common issue known as the Performance Gap Digital twins provide tools that we can use to make sure these buildings perform as anticipated. They can also help to verify contractors’ work, check their homework and make sure they do what they were supposed to do. Right first time. Enables collaboration across all parties
• #19: Existing building - University Library Significant savings to be made in existing buildings through BMS optimization as shown here. AHU Logic problems, seasonal issues with operation identified
• #20: Building on these initial findings, IES began to explore how the digital twin could be used to support future scenario planning across the campus, to achieve the overarching goal of net-zero by 2030. After previous analysis had highlighted an absence of lighting controls in the Library (limiting opportunities for optimisation during periods of low occupancy) one of the first scenarios tested within the campus level digital twin focused on upgrading the lighting across the entire campus to LED installations with dimming controls. Another scenario explored the impact of improving the thermal envelope of all buildings across the campus with a condition rated lower than D.
• #21: Example results demonstrated at the campus level through two of the high-level scenarios tested. This highlights the value of the technology as a key decision making tool. Some interventions are no-brainers – others are good to understand the impact of but probably aren’t realistic/going to fly.. Scenario 1 (LED lighting + dimming controls) = Estimated 16% of total energy consumption (4667MWh) and over £1million savings per year Scenario 2 (envelope upgrades for buildings rated D or lower) = Estimated 2% of total energy consumption (1306MWh) and over £50k savings per year
• #22: The digital twin of the district heat network (DHN) was then also used to support portfolio level planning across the campus. The university wanted to know if it was possible to move to a low heat temperature network which would enable technologies such as heat pumps, which would have a significant carbon reduction compared to the current CHP-gas solution. Initial study identified a 40% potential increase in system efficiency, however further detailed analysis is required to fully investigate the feasibility of this solution. A high-level analysis was undertaken to show that this was possible, and would not only enable the University to decarbonise their heating source, but it would also create an increased efficiency in the overall DHN system of up to 40%.
• #24: This slide introduces how IES got engaged with University of Liverpool and what was the first stage of the DT project, aimed at assessing the HVAC and lighting refurbishment project that was ongoing at the Foundation Building.
• #25: The first version of the DT was created in 2022, representing energy flow across spaces and rooms within the building. Bar chart shows measured vs simulated monthly electricity consumption, showing that the model closely represented the actual building. At this stage, the model was calibrated using historic monthly measured data. The new HVAC system was then virtually tested on the model.
• #26: This slide describes the refurbishment tested on the model and shows 14% savings as predicted by the digital twin. We will see in a second that these savings have been now verified (and exceeded) after implementation.
• #27: Post-refurbishment, the calibrated energy model that was created during the earlier phase of the project was upgraded to a Digital Twin, using live hourly BMS and metered data from the building post refurbishment. This slide shows the live data flowing to the IES Live online platform. Sensor location in the building and their status compared to a pre-defined range are also provided. You can open the PowerBi dashboard by clicking the image on this slide.
• #28: Here you can see another page of the IES Live operational dashboard with overview energy use in real time, and KPIs updated accordingly. You can select a range (2 days, 1 month, 1 year) and the graph and KPI cards will update automatically. KPIs displayed include: BMS, temp, CO2, humidity and energy meters Recently occupancy added - cost per occupant interest Connection - Our API to BMS and tells us what naming connection, format to read.
• #29: Here you can see how the simulated data from the Digital Twin flows live to the IES Live platform and is compared with the actual building consumption and an example of operational strategy tested on it (modifying setpoints for heating and cooling) and the potential savings Model on cloud, access online. Baseline display metered real time Proposed=scenarios if measures implemented
• #30: Here you can see the project tracking page where we assess in real time the impact of the HVAC refurbishment : between April-Dec 2023, a 23% reduction in energy consumption and £25k saving in operational cost was verified, and these savings continue over time. Difference from the predicted savings in Phase 1 can be ascribed to the better accuracy of the latest model used to evaluate the actual savings. Our cost £18k No spend to achieve savings
• #31: For the Live data platform, we are working on connecting occupancy data to display them with actual energy use in real time .
• #32: We tested a number of CAPEX interventions on the upgraded DT. They included glazing upgrades, solar shading, air source heat pumps for domestic hot water, a water source heat pump compared to VRF, PV generation and a combination of all ECMs. Percentage reduction in energy consumption for each measure, and the combined savings, are shown here.
• #33: To facilitate the visualisation of the results by different stakeholders, an interactive dashboard was developed where you can navigate the impact of different measures, edit capital cost and energy prices to get updated ROI. All the interventions were sequenced on a roadmap, including also grid decarbonisation. Clicking on the image you can open the PowerBI dashboard if you want to show it live
• #35: 2018/2019 The university has campuses at different locations around the world. This pilot project looked into three buildings in the Heriot-Watt Edinburgh Campus using IES Digital Twin technology to explore how the buildings perform, and monitor their behaviour. The project focused on different types of buildings, with different types of available data. The aim was to ultimately create a command centre that first looks at the overarching campus level, and can be investigated down to individual building level.
• #36: The three buildings of initial focus for this pilot project were: •The Global, Research, Innovation and Discovery building (GRID) – built in 2019, mixed use, VRV heating/cooling.  • Edwin Chadwick building – built in 1987, mixed use but with more cellular offices, seminar rooms, lecture rooms and workshop spaces, gas heating. • Robert Bryson Hall – built in 1992, halls of residence, gas heating. This is the building referenced in the slides. The three buildings of focus were all data rich in terms of real-world information, therefore the models were able to be refined and detailed in the VE to allow for better calibration of systems performance and options analysis for energy improvement. Using this model IES conducted early stage analysis, simulating and comparing multiple scenarios. A campus model was also created as a mechanism to visualise which buildings have undergone calibration and monitoring and track the performance of the buildings live.
• #37: This slide shows the results of the ECM analysis for Robert Bryson Halls and the Edwin Chadwick Building Robert Bryson Halls 44% total energy savings simulated 23% energy cost savings simulated 39% CO2 saving simulated Edwin Chadwick 43% total energy savings simulated 16% energy cost saving simulated 36% CO2 saving simulated
• #39: To conclude, Digital Twins can enable…. Better design for both buildings and campuses, making better decisions from the outset Better control for both buildings and campuses, optimising energy use, while maintaining health and well-being Better information on which to make investment decisions Better planning and communication of net zero roadmap interventions Better tracking and verification of expected savings post implementation