A leading Southeast Asian bank with a presence in 19 countries partnered with Ecolibrium to optimise its HVAC operations and improve indoor air quality at its Singapore headquarters. With a total built-up area of 42,530 m² across 66 floors ,and the bank aimed to achieve AI HVAC optimisation for indoor air quality using Ecolibrium SmartSense insights.
Outcome Summary :
- 3% energy cost savings
- 15% improvement in indoor air quality (IAQ)
The Challenge in HVAC Optimisation
The bank aimed to optimise HVAC performance using AI/ML while reducing energy and maintenance costs. However, achieving optimal IAQ without increasing costs proved difficult. AHU setpoints did not adapt to external conditions, so systems ran inefficiently during low-occupancy hours.
In addition, faulty sensors reduced data accuracy, while weak alignment between outdoor weather and internal cooling demand further impacted efficiency. Static setpoints led to overcooling, discomfort, and energy waste. As a result, the building experienced both inefficiency and inconsistent occupant comfort.
AI HVAC Optimisation for Indoor Air Quality in Action
Ecolibrium SmartSense was deployed on two pilot floors, integrating 464 data points and 27 energy meters. Real-time data was collected from IAQ sensors, AHUs, and energy meters, while Cooling Degree Day (CDD) models were applied to evaluate demand-based cooling. Machine learning models revealed that AHU setpoints had only 13–20% correlation with ambient temperature and CDD data, confirming that cooling was not dynamically aligned with actual conditions. Overcooling was particularly evident during low-occupancy periods. To resolve the challenges, we implemented Operational excellence, energy management and human wellness index as solutions.
The Outcome
Ecolibrium SmartSense analysis led to a recalibration of AHU setpoints from 14°C to 17–18°C, significantly reducing overcooling. Balanced air circulation was achieved while maintaining thermal comfort, and IAQ levels improved by 15%. These changes also resulted in 3% energy cost savings, meeting the client’s goals for operational excellence and sustainability. The client began incorporating Ecolibrium SmartSense’s operational recommendations and committed to scaling the platform across all 12 of its sites in Singapore.
The Impact
Ecolibrium SmartSense’s ML models revealed a weak correlation between external ambient conditions and AHU behaviour, highlighting the need for internal logic optimisation. By integrating IAQ data, CDD metrics, and AHU performance, Ecolibrium SmartSense provided clear, actionable insights. The recalibrated setpoints eliminated unnecessary cooling, improved comfort, and reduced energy waste. The client validated the impact and adopted Ecolibrium SmartSense’s insights into daily operations, enhancing building performance and occupant well-being.
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