The document discusses the targets and digital twin development of a university campus to achieve net zero carbon emissions by 2030. It aims to digitize energy processes through building and network modeling. A library is used as a test case to model energy usage under different scenarios and compare actual vs modeled consumption. The digital approach is expected to enable energy reduction, access new funding solutions, improve data quality, encourage collaboration, and allow for better carbon assessment and visualization.

1. Future of the Higher Education
Estate
Impact of COVID -19 and
Net Zero Targets

2. Contents
• The current situation
• Our targets
• Building the relationships
• Digital Twin development
• The Library – a COVID example
• Benefits to the digital approach

3. The current situation
• A university of 3 parts
• 52920 tonnes carbon emissions
in 2020
• Lack of granular information
• Building the future

4. Our targets
• Net Zero by 2030
• Large scale energy changes
• Dramatic reductions in fossil fuels for
heat generation
• Digitizing the energy process
through building and network
modelling

5. Building the relationships
• Common goal
• Mutual benefit
• New ideas and solutions
• Finance solutions

6. Digital Twin Development
The Living Lab approach
• Building selection
• Prominence
• Data sources
• Collaborative working
• Identifying what success looks
like
Caution Points
• Opinions (BIM vs DT)
• Data sources
• What you have Vs what you want
• Right team to start
• IT
• Data
• BMS
• Sensors

7. The COVID impact – Library test case
• Three specific tests using the VE
and DT data
• The future prediction test
• People Vs Consumption
• Actual Vs Model

8. The future prediction test
0
5000
10000
15000
20000
25000
30000
35000
Base line data People removed People and
associated
people loads
removed
People and
associated
people loads
removed
reduction in
temperature
People and
associated people
loads
removed
reduction in
temperature
increase ACH
MwH
Function Situation
Energy Breakdown Library Modelling
Total Electricity Total Gas
Chillers energy Equipment electricity
Lights electricity Boilers space condition energy
DHW energy

9. Actual Vs Modelled
0
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20
30
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50
60
70
80
90
0
5000
10000
15000
20000
25000
30000
06-Jan
12-Jan
18-Jan
24-Jan
30-Jan
05-Feb
11-Feb
17-Feb
23-Feb
29-Feb
06-Mar
12-Mar
18-Mar
24-Mar
30-Mar
05-Apr
11-Apr
17-Apr
23-Apr
29-Apr
05-May
11-May
17-May
23-May
29-May
04-Jun
10-Jun
16-Jun
22-Jun
28-Jun
04-Jul
10-Jul
16-Jul
22-Jul
28-Jul
03-Aug
09-Aug
15-Aug
21-Aug
27-Aug
02-Sep
08-Sep
14-Sep
20-Sep
26-Sep
02-Oct
08-Oct
14-Oct
20-Oct
26-Oct
01-Nov
07-Nov
13-Nov
19-Nov
25-Nov
01-Dec
07-Dec
13-Dec
19-Dec
25-Dec
31-Dec
<0
1/
01
/2
02
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
%
change
kWh
Axis Title
Variance between modelled and actual data
Sum of person Count Sum of KWH consumption Actual data Sum of KWH Consumption Modelled data Phase 3 Sum of % variance from actual data

10. Benefits to the digital approach
• Energy Reduction
• Funding solutions
• Better Data
• Right first time solution
• Evidence
• Carbon assessment
• Visualisation for encouragement
• Collaboration

11. #UofGWorldChangers
@UofGlasgow
Thank you
Gillian Brown
Gillian.brown.2@glasgow.ac.uk
07701 371552