Link has implemented Energy Management System (EMS) with A.I. optimization at T Town in Tin Shui Wai to improve energy efficiency since December 2021. The system uses big data analysis of building information with a machine learning model to identify energy-saving opportunities and optimize operation.
Built on top of the existing building management system (BMS), the EMS collects real-time data and control on-site field equipment. The data is processed via a cloud service and presented at an online energy dashboard with notification for real-time monitoring. EMS also features a fault detection and diagnostic feature that detects abnormal operation to avoid energy wastage and observe irregular data for suggesting predictive maintenance.
In addition, the project utilizes A.I. technology for cooling load prediction and chiller plant optimization. The machine learning algorithm predicts the building's required cooling load, selects the most energy-efficient chiller running sequence, optimizes the centralized A/C chilled water temperature for energy-saving etc. EMS with AI models would continuously analysis and improve based on dynamic data to achieve on-going commissioning.
The project adopted industry-standard International Performance Measurement and Verification Protocol (IPMVP) – Option C for measurement and verification of energy savings. The yearly electricity saving is 5.7% versus baseline, and the chiller plant coefficient of performance (COP) enhances by 7.8%. The project achieves these savings while maintaining indoor air quality.
The success of the T Town EMS has led Link to deploy EMS to all their shopping centers with centralized air conditioning system. Each year, EMS deployment is anticipated to save 5.8M kWh of energy or equivalent to the electricity consumption of more than 1,700 Hong Kong households and reduce 3,300 ton CO2e emissions.
The implementation of EMS with A.I. optimization provided an example of how technology can be used to improve energy efficiency and reduce carbon footprint.