• Identified a 5% energy-saving potential in AHUs through our proprietary machine learning algorithms.
• Achieved a 75% reduction in HVAC maintenance frequency by shifting to bi-monthly schedules.
• Forecasted 60% maintenance cost savings with a transition to quarterly predictive maintenance.
A prominent Southeast Asian bank with a presence in 19 countries initiated a project with Ecolibrium to optimise the HVAC system at their 25-storey Singapore offices. The project aimed to extend the maintenance cycles of their decade-old system, reduce energy consumption, and enhance occupant comfort. The bank sought to transition from a costly monthly OEM-directed maintenance schedule to a more efficient and predictive bi-monthly regimen.
Faced with ageing infrastructure and outdated BMS capabilities, the client's primary objective was to extend HVAC maintenance intervals and simultaneously identify opportunities for energy savings. To set this in motion, our engineers conducted a comprehensive assessment, providing a thorough evaluation of the current BMS and HVAC infrastructure.
A phased approach was designed to validate the effectiveness of SmartSense in real-time operational environments. By prioritising critical areas for immediate attention, such as air-side optimisation and the recalibration of degraded sensors, the client was prepared to redefine maintenance strategies from the ground up, ensuring that accurate data and actionable insights backed each step taken.
In the first quarter of 2023, SmartSense was strategically deployed on two floors of the bank's 25-story building to enhance the data collection and performance analysis of key HVAC components. The project resulted in the integration of 464 data points and 27 energy meters, providing a level of detail in system operations previously unattainable with the existing BMS. The platform's advanced algorithms generated a wealth of insights and recommendations, which were thoroughly reviewed in a comprehensive workshop with the client.
The insights from SmartSense indicated significant inefficiencies during periods of low occupancy, with chillers operating at high kW/RT despite low delta temperatures. This prompted the retrofitting of advanced TDS, TSS, and pH sensors onto the cooling towers to enable precise water quality monitoring and intelligent modulation based on the wet bulb temperature. The adjustments led to smarter staging strategies and maintenance schedules, which are crucial for the energy-efficient operation of the HVAC system in Singapore's tropical climate.
By mid-2023, SmartSense's detailed analytics had detected critical anomalies in the supply air temperatures of the AHUs and associated PFCUs, alongside elevated CO2 levels in the return air, compromising air quality and energy efficiency. Addressing these issues through recalibration and strategic filter replacements in the PFCUs optimised equipment performance and improved air quality. These interventions enhanced occupant comfort and wellness, confirmed by the reduced CO2 ppm levels measured at individual workstations.
SmartSense's machine learning algorithms continued to guide operational efficiency improvements as the year progressed, leading to prolonged maintenance intervals and enhanced equipment health indices. By the end of the third quarter, these advancements had established a new benchmark for the bank's operational practices, highlighting the tangible benefits of the SmartSense deployment and presenting a convincing case for extending these data-driven maintenance practices to the entire building.
The initial success of this project has not only validated SmartSense's capability to enhance HVAC maintenance cycles but also demonstrated how it can guide a shift towards predictive, condition-based maintenance. With SmartSense's insights leading to substantial cost savings and identifying numerous energy-saving measures, the client is preparing for a full-scale implementation across their HVAC system, a significant step towards enhancing operational efficiency and occupant wellness.
SmartSense's proprietary AI algorithms played a pivotal role in this transition, with the creation of a digital twin and the establishment of KPIs for equipment health and environmental conditions proving critical. The platform's comprehensive analysis detected key opportunities for maintenance optimisation, underpinning the client's decision to expand the initiative. The successful application at this facility presents a promising case for scaling up such data-driven approaches across the banking sector.
Through SmartSense, we have showcased the transformative capabilities of AI-powered asset optimisation. By guiding a prominent Southeast Asian bank in Singapore towards predictive maintenance, we've highlighted the tangible benefits of integrating our advanced technology into operational practices.
Our commitment is to lead organisations toward a more sustainable, efficient, and cost-effective future. If you are interested in discovering how SmartSense can redefine your organisation's maintenance strategies, reach out to our team today to schedule a personalised demo and consultation.