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.

1. Digital Twins
for the Built
Environment
Dr Sarah Graham
ICL Consultant
Adalberto Guerra Cabrera
Senior R&D Consultant

2. Industry 4.0
Source: UNIDOwww.iesve.com

4. What is a
Digital Twin?
www.iesve.com

5. Manufacturing/Automotive
www.iesve.com Source: Wipro

6. UK National
Digital Twin
www.iesve.com 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.”

7. $73 million, data-rich, live digital
replica of Singapore
www.iesve.com

8. A Climate
Emergency
www.iesve.comwww.iesve.com

9. Scenarios that give a 66% chance of
holding temperature rise below 1.5C
throughout this century are “already
out of reach”
IPCC
www.iesve.com

10. www.iesve.com
Concern over Climate
Change is on the rise
Ipsos Mori

11. www.iesve.com
“Our one material
advantage is in the
accelerating burst
of green technologies”
Jeremy Grantham

12. Digital Twins for the Built Environment
www.iesve.comwww.iesve.com

13. www.iesve.com
The 5 Pillars of a
Decentralised,
Decarbonised &
Digitalised Energy
Landscape
www.iesve.com Source: MefCO2

14. Energy Efficient
Decarbonizing Sustainable
Digital Twin
 Citizens
 Companies
 Campuses
 Communities
 Cities
 Countries
www.iesve.com

15. Maximise Value:
Improve
Operational
Performance
www.iesve.com

16. Untapped Operational Data
www.iesve.com

17. One Single Platform
www.iesve.com

18. New IOT Technology
www.iesve.com

19. The Required Information at the Required Level
(i.e. Command Centres)
www.iesve.com

20. Advanced
AnalyticsAdalberto Guerra Cabrera
Senior R&D Consultant
www.iesve.com

21. Combining different
data sets
Advanced Analytics
Finding data trends using
plots
Other methods of data
analysis/visualisation
www.iesve.com

22. Machine Learning (ML) & Artificial
Intelligence (AI)
www.iesve.com Source: Towards Data Science

23. Machine Learning (ML) & Deep Learning (DL)
www.iesve.com Figure Source: Microsoft Azure Machine Learning Studio
“Closed”

24. Data Gaps
www.iesve.com
Ensuring Data Quality is Difficult
Often there is Missing Data

25. Data Gaps
Possible to Fill Data Gaps using:
 Daily, Weekly, Monthly &
Holiday Trends
 Physics Based Simulation
www.iesve.com

26. 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
www.iesve.com
ACMV.CHILLER.3.CDWS
CH03 CW ST
CONDENSER 04 WATER
SUPPLY TEMPERATURE
DG/KBTB/CWBTU3/DTP_1
ABB_CH3 CWSV1

27. 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
www.iesve.com
Original name
Tags

28. 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
www.iesve.com

29. 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.
www.iesve.com

30. 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
www.iesve.com

31. Energy Use/Generation
 Energy Demand or Renewable Generation predictions
 Created from historical data and weather forecasts
www.iesve.com
PV model Measured

32. 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
www.iesve.com

33. Practical
Applications
of Digital
Twins

35. 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

36.  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
www.iesve.com

37.  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

38.  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
www.iesve.com

39.  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

40.  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
www.iesve.com

41.  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
www.iesve.com

42. Any
Questions?

43. Thank You
sarah.graham@iesve.com
0141 945 8500
www.iesve.com/icl