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IES Faculty: Digital Twins – the latest buzz word! But what does it mean for the built environment?


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The concept of digital twins is not new. However, the advance of the ‘fourth industrial revolution’ is making cyber-physical systems in the built environment practical, useful and affordable. Productivity gains and cost savings aside, digital twins will also become an essential tool in addressing the current climate crisis, presenting dramatic and exciting opportunities to achieve zero carbon targets and promote sustainable, healthier buildings.

But what are digital twins? And how can they be used in the built environment? What tools are available? And how can we overcome the challenges faced by lack of data? This faculty answered these questions.

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IES Faculty: Digital Twins – the latest buzz word! But what does it mean for the built environment?

  1. 1. Digital Twins for the Built Environment Dr Sarah Graham ICL Consultant Adalberto Guerra Cabrera Senior R&D Consultant
  2. 2. Industry 4.0 Source:
  3. 3. What is a Digital Twin?
  4. 4. Manufacturing/Automotive Source: Wipro
  5. 5. UK National Digital Twin Source: BIM Plus “Greater data sharing could release an additional £7bn per year of benefits across the UK infrastructure sectors, equivalent to 25% of total spend.”
  6. 6. $73 million, data-rich, live digital replica of Singapore
  7. 7. A Climate Emergency
  8. 8. Scenarios that give a 66% chance of holding temperature rise below 1.5C throughout this century are “already out of reach” IPCC
  9. 9. Concern over Climate Change is on the rise Ipsos Mori
  10. 10. “Our one material advantage is in the accelerating burst of green technologies” Jeremy Grantham
  11. 11. Digital Twins for the Built Environment
  12. 12. The 5 Pillars of a Decentralised, Decarbonised & Digitalised Energy Landscape Source: MefCO2
  13. 13. Energy Efficient Decarbonizing Sustainable Digital Twin  Citizens  Companies  Campuses  Communities  Cities  Countries
  14. 14. Maximise Value: Improve Operational Performance
  15. 15. Untapped Operational Data
  16. 16. One Single Platform
  17. 17. New IOT Technology
  18. 18. The Required Information at the Required Level (i.e. Command Centres)
  19. 19. Advanced AnalyticsAdalberto Guerra Cabrera Senior R&D Consultant
  20. 20. Combining different data sets Advanced Analytics Finding data trends using plots Other methods of data analysis/visualisation
  21. 21. Machine Learning (ML) & Artificial Intelligence (AI) Source: Towards Data Science
  22. 22. Machine Learning (ML) & Deep Learning (DL) Figure Source: Microsoft Azure Machine Learning Studio “Closed”
  23. 23. Data Gaps Ensuring Data Quality is Difficult Often there is Missing Data
  24. 24. Data Gaps Possible to Fill Data Gaps using:  Daily, Weekly, Monthly & Holiday Trends  Physics Based Simulation
  25. 25. Data Organisation  Natural Inconsistency In Naming Of Sensor Points & Data Sets  Each Building And System Set Up By Different People  Issue Unique To The Building Industry ACMV.CHILLER.3.CDWS CH03 CW ST CONDENSER 04 WATER SUPPLY TEMPERATURE DG/KBTB/CWBTU3/DTP_1 ABB_CH3 CWSV1
  26. 26. Original name Tags Data Organisation  Labelling standards such as Project Haystack utilized  ML makes this manual process quick and easy  Saves time: 1 week to 3 hours on a large project Original name Tags
  27. 27. Advanced vs Traditional Analytics  Advanced analytics enables continuous assessment of issues in a building  Large number of variables can be analyzed at the same time as soon as they are uploaded  “Live” comparisons allow energy-related issues to be flagged & tackled as soon as they appear  Saves money, reduces energy wastage
  28. 28. Anomaly Detection  How do you know when a value is a potential fault?  AI used to learn ‘expected range’ from historic data  Includes consideration of weather and other variables  Values outside are potential sensor failures, system malfunctions, comfort issues, etc.
  29. 29. Operational Drift  Adjust last year’s energy to this year’s weather  Identify drifts from expected performance and correct  Continued trends in high demand indicate operational drift
  30. 30. Energy Use/Generation  Energy Demand or Renewable Generation predictions  Created from historical data and weather forecasts PV model Measured
  31. 31. Advanced Analytics End goal  Keep internal conditions within acceptable levels with minimum energy  React to and address recurrent problems before they happen  Avoid costly downtime and complaints  Accurate prediction of baseline energy use and savings estimations
  32. 32. Practical Applications of Digital Twins
  33. 33. Tackle Energy Hungry Buildings  Data driven operational improvements  Annual savings of £52.3k  Gas savings of 26%  Payback period of less than 6 months RIVERSIDE MUSEUM Glasgow, United Kingdom
  34. 34.  30 Schools, 100+ Sensors, 1500+ Data Channels  Data from 7 Providers into 1 Central Interface  Energy/Water Use & Predictive Maintenance  Temperature Alerts (Occupant Comfort & Water)  PV Panel Performance & Fault Detection  Missing Data Alerts (Ensuring Data Completeness) Improve Performance Across a Portfolio CITY COUNCIL United Kingdom
  35. 35.  90 acre campus  Wind Turbine meets 52.8% of demand  Electricity Storage meets only 2.2% of demand  10.5% Transformer Losses  45% of demand is met by the grid Assess Renewable Energy Networks DUNDALK INSTITUTE OF TECHNOLOGY Dundalk, Ireland Wind Turbine generation greater than demand Stored electricity used until depleted
  36. 36.  Lighthouse City for +CityxChange (Smart City EU H2020 Project)  Project just launched: Aim to create clusters of buildings that are positive energy producers  Phase 1: Upgrade of existing building stock within the Georgian district  A holistic approach to energy, transport, building, and social development Deliver Positive Energy City Blocks Limerick Ireland
  37. 37.  145+ low-energy homes  Real-time data in an interactive 3D Energy Model  Integrated solar and thermal ground source energy generation  Largest community energy battery in Europe  Ground breaking community energy scheme  147 inch Touch Screen TRENT BASIN Nottingham, United Kingdom Engage a Community in Real-Time
  38. 38.  Over 200 buildings across 250 hectares  Vision to become the greenest campus in the world  Sustainable masterplanning & building performance optimisation  Impact of retrofit measures virtually tested Potential for:  31% energy reduction  S$4.75M cost savings  9.6kt Carbon reduction Deliver an EcoCampus Nanyang Technological University (NTU) Singapore
  39. 39.  Island community looking to optimise the use of local energy & become net-zero  Whole Island Energy Network Model created  76% Total energy savings identified  Payback of 6 years or less  Combination of building energy efficiency, grid improvements, distributed energy generation & community storage Help an Island become Net-Zero Eday Orkney Islands
  40. 40. Any Questions?
  41. 41. Thank You 0141 945 8500