Portfolio of Bank Buildings

Singapore 2020

Qi Square Pte Ltd have used IESVE software as part of their Virtual Audits process to perform calibrated modelling and investigate energy efficiency improvements for a portfolio of bank buildings in Singapore.

Key Facts

  • Virtual Audit for portfolio of 15 bank offices
  • Models calibrated within 98% accuracy of actual buildings
  • ECMs tested to identify 27% electricity savings across the portfolio
  • PV installations determined to cover an additional 19% of electricity demand

IES partner, Qi Square Pte Ltd have been using IES Digital Twin technology as part of a unique Virtual Audits (VA) process to replicate the actual performance of a portfolio of buildings for a large bank in Singapore. The process aims to provide a holistic overview of building performance and facilitate accurate assessment of energy efficiency improvements at a portfolio level in a more efficient and cost-effective way than traditional on-site energy audits.

The Qi Square team worked in collaboration with IES on this project, using their Virtual Environment (VE) software as the main simulation tool. The project considered a portfolio of 15 individual branch offices, comprising approximately 111,000m2 of net area. 

The process began with the creation of detailed VE energy models for each of the 15 offices using design drawings and other known data regarding the buildings’ construction properties, systems and equipment, occupancy and operating schedules. These models were then calibrated using available energy consumption data from the client’s utility bills to determine the baseline Energy Usage Intensity (EUI) of the offices and to align the models more closely with the actual operational performance of the buildings.
 
The team were able to achieve a high level of accuracy through the calibration exercise, with all of the models achieving at least 98% accuracy when compared to the actual buildings in operation.  Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CVRMSE) are the two statistical indices that are widely applied for evaluating calibrated models, with ASHRAE Guideline 14-2014 setting the target for monthly-based calibrations as within ±5% for NMBE and 15% for CVRMSE. The calibration results for the portfolio fell well within these thresholds, achieving an NMBE and CVRMSE of 0.98% and 2.46% respectively, providing an accurate baseline from which to determine the impact of potential improvements across the portfolio.

With these accurate digital twin baselines in place, the team tested a series of Energy Conservation Measures (ECMs) within the models to determine the energy savings potential across the portfolio. First, they considered retro-commissioning measures which would involve little or no capital expenditure to the client. These improvements focused on simple operational changes, for example, changes to cooling set points and scheduled thermostat setbacks, introduction of occupancy-based controls and improvements to occupant use, amongst other factors.

The team then considered potential retrofitting measures which the client could invest in to further improve performance across the portfolio. Proposed measures included building envelope, lighting and equipment retrofits, as well as upgrades to the HVAC systems.

The project aimed to target savings of 25% across the whole portfolio. End results demonstrated that this target could be achieved, with the total simulated savings after applying the proposed ECMs calculated at 27%.

As a final step, the team also conducted a PV assessment to determine the annual renewable yield potential at each site. The simulation results showed that an additional 19% of the client’s electricity needs could be met by PV installations, helping to further improve efficiency across the portfolio and reduce operational costs. 

IESVE proved to be an invaluable tool for carrying out Qi Square’s Virtual Audit process. The project successfully demonstrated viable solutions to improve energy performance on a portfolio level and will allow the client to make informed decisions on how to realise their energy efficiency goals.

For a more detailed overview of this project, read the full paper here.