The document discusses the operational energy reduction achieved through data analysis and virtual benchmarking in a case study at Barts Health NHS, highlighting the performance gap between expected and actual building energy use. It emphasizes the importance of effective data management, calibration of operational models, and identification of energy conservation measures, resulting in significant reductions in gas and electricity consumption. Overall, the study indicates that energy cost and carbon emissions can be lowered through an integrated approach to data analysis and building performance optimization.

1. Operational Energy Reduction
through Data Analysis & Virtual
Benchmarking
Case Study: Alex Wing, Royal
London Hospital
Darragh Gleeson
Senior Project ConsultantIES

2. Introduction
1. Digital Building Models
2. The Performance Gap
3. Data Analysis
4. Operational Models
5. Case Study – Barts NHS

3. Integrated Environmental
Solutions (IES)
Scottish Green Tech
Company Founded in 1994
• Focused on reducing the energy
& carbon footprint of new and
existing buildings
UK Market Leader and
Globally Established
• 160+ employees
Head Office in Glasgow
• Offices in Dublin, Atlanta &
Pune (Mumbai)
• Presence in Europe, Australia,
Singapore, Middle East & Africa
Over 20 years of Sustainable Design
About IES

4. Virtual Environment

5. The performance gap
There is a mismatch between the expectations
around the performance of new buildings and
the reality of the utility bills.
This difference between expected and realised
energy performance has come to be known as
the “performance gap”

6. Why is there a performance gap?
• Regulatory/compliance models used for
predictions of in-use energy
• Discrepancies between design documents and
as-built
• Innovative systems not fully understood
• Poor commissioning
• Lack of monitoring and feedback

7. DATA in Buildings

8. Untapped Operational Data

9. More data captured and managed
= clearer picture of your building’s performance
Using Data to build a complete picture
With Analysis Data is Powerful
IES can deliver a robust data collection and analysis strategy:
• Effective Logging
• Well organised and managed, with clear naming conventions
• Gaps identified& filled using simulation
• Stored for a long time period, in a manner that is easily accessible
• Analyse data and find opportunities

10. IES-SCAN
• Online Platform
• Data Collection& Analysis
• Links to BIM and energy model of building
• Software for More Accurate Calibration

11. Benefits of Data Analysis
• Commissioning /Controls issues
• £60k of “quick win” savings
• Payback in under 6 months
Signal%
Temperature(°C)
AHU8 Reclaim Duct Temp Supply Fan Speed Heating Valve

12. Operational Models
Dynamic Simulation Model
+ Operational Data
+ Calibration
Actual
Building
Gap between predicted and
actual performancecan be
closed

13. Parametric & Hone
• Parametric – Sensitivity Analysis
• Hone – Optimisation
• Faster Calibration

14. 1. “What if…?” Scenarios
• Savings vs Occupant Comfort Predictions
2. Energy Conservation / Retrofit Measures
• Virtual Testing & Validation
• Measurement & Verification
3. ContinuousCommissioning
• Operational Drift Identification
• Fault Detection
Benefits of Operational Models

15. CASE STUDY

16. Barts Health NHS: Alex Wing
• Review of Building Data through IES SCAN
• Calibrated Operational Model
• Identify & Verify Savings

17. Barts Health NHS: Alex Wing
• Simulation model built in IESVE software
• Design Stage Information + Logbook data

18. Barts Health NHS: Alex Wing
• Simulation model built in IESVE software
• Design Stage Information + Logbook data
• Model enriched via SCAN
– BMS Data
– Submeter Data
– Real Weather Data

19. Simulation parameters tweaked to provide a
close match to measured consumption
Simulated Electrical
Consumption against
measured electrical
consumption.
Nov 2015
Barts Health NHS: Alex Wing

20. End Use CVRMSE NMBE
Electricity (Monthly) 2.1 -0.4
Electricity (Hourly) 14.3 -0.8
Gas (Monthly) 8.6 +0.4
HVAC (Monthly) 6.1 -3.4
Small Power (Monthly) 2.5 +2.2
TARGET 15 ± 5%
• Simulated Electricity calibrated to both Monthly & Hourly
targets.
• Simulated Gas calibrated to Monthly & Daily targets.
Alex Wing Benchmark Model Results
Barts Health NHS: Alex Wing

21. Verify the impact of Energy Conservation
Measures (ECMs)
• DHW Secondary Circulation
• Ventilation Run Hours
• Gas reduced by 29.4%
• Electricity reduced by 6.2%
Barts Health NHS: Alex Wing
2,658
2,221
1,877
2,083
0
500
1000
1500
2000
2500
3000
Gas Electricity
MWh
Benchmark Combined ECMs

22. Identify additional issues and predict savings
• Demand Control Ventilation
• Holiday Schedule
• Weekend Secondary Circulation
• Further Savings:
– Gas 18.7%
– Electricity 3.5%
Barts Health NHS: Alex Wing

23. Energy Conservation Measures Reviewed
– Changes to Control Strategies
– Changes to Plant Operation and Schedules
– Installationof Solar PV & CHP
– All in combination
0
1000
2000
3000
4000
5000
6000
Benchmark 01 Benchmark 02 Benchmark 03 Final Model
MWh
Gas Electricity
Barts Health NHS: Alex Wing
Predicted Savings Overall:
– Gas Energy reduced by
22.5%
– Electricity reduced by
30%
– Utility cost reduced by
28.2%
– Carbon emissions
reduced by 27.5%

24. An Integrated Approach

25. darragh.gleeson@iesve.com
0141 945 8500
www.iesve.com
www.iesve.com/DiscoverIES
Darragh Gleeson
Thank You