Technical Overview
As the era of Industry 4.0 continues to deepen, digital and intelligent management of chiller rooms has become a core component of modern building energy management. The Cloud-zhilian Energy Efficiency Management Platform, through the deep integration of IT, big data, and artificial intelligence technologies, has created a “smart brain” for refrigeration systems, enabling real-time monitoring of room operating conditions, precise analysis of energy efficiency levels, and predictive maintenance for equipment health. This innovative technology is driving the transformation of refrigeration systems from traditional “human-supervised” models to smart operation and maintenance models characterized by “unmanned inspections and minimal staffing,” bringing about a revolutionary breakthrough in enterprise energy management.
In the context of today’s smart manufacturing and green building trends, the chiller room—a key energy-consuming component of a building—directly impacts a company’s operational costs and carbon emissions levels through its operational efficiency. The Cloud-Intelligence Integration Platform builds a digital twin system that fully maps the physical chiller room into a virtual space, enabling visualized monitoring of operating conditions, real-time assessment of energy efficiency, and intelligent early warning of potential faults. This platform is not only suitable for new construction projects but can also be used to intelligently upgrade existing chiller systems, helping companies improve energy efficiency and optimize operations and maintenance without replacing major equipment.
Core principle
1. The platform adopts an innovative four-layer architectural framework to ensure efficient system operation and intelligent decision-making. At the perception layer, IT devices such as smart meters, temperature sensors, pressure sensors, and flow meters are deployed to build a data acquisition network that covers the entire system, enabling real-time collection of equipment operating parameters, energy consumption data, and environmental indicators. These data are transmitted via high-speed networks like 5G and industrial Ethernet to the cloud platform, guaranteeing both the real-time nature and security of data transmission.
2. At the platform layer, the data mid-end built on big data technologies is responsible for cleaning, storing, and mining and analyzing massive volumes of operational data. By establishing equipment energy-efficiency models, fault-prediction models, and optimized-operation models, the platform can deeply uncover the value hidden in the data. In particular, machine-learning algorithms, through in-depth analysis of historical operational data and real-time monitoring data, autonomously identify optimal operating strategies, thereby enabling intelligent control of refrigeration systems. The application of digital twin technology allows the platform to accurately simulate and predict equipment operating conditions, providing a scientific basis for preventive maintenance.
3. The application layer provides users with a wealth of functional modules, including a visual monitoring interface, energy efficiency analysis reports, an intelligent alert center, and optimized strategy recommendations. The system supports multi-device access, enabling users to monitor the system’s operational status in real time via PC or mobile devices and receive smart push notifications with operation optimization suggestions and maintenance reminders. The platform also offers a rich set of API interfaces, supporting deep integration with building automation systems, enterprise ERP systems, and other platforms, thereby achieving data interconnectivity and business collaboration.
Application advantages
1. Precise Energy Efficiency Management: The platform can calculate the system’s SCOP in real time, automatically detect energy consumption anomalies, and provide optimized operating strategies to continuously enhance system energy efficiency.
2. Intelligent Operations and Maintenance Alerts: Based on equipment operating characteristics, a health model is established to provide early warnings—7 to 30 days in advance—of potential failures, shifting from reactive emergency repairs to proactive maintenance and reducing unplanned downtime.
3. Efficient and Transparent Management: Supports synchronized access via both PC and mobile devices, enabling remote monitoring, energy efficiency benchmarking, and automatic report generation—boosting management efficiency by more than 50%.
4. Decision-making data support: Provide multi-dimensional energy consumption analysis reports and carbon emission data to offer scientifically sound data support for enterprises’ energy-saving upgrades and carbon neutrality strategies.
5. Seamless System Integration: Adopting an open architecture, the system can seamlessly integrate with Building Automation Systems (BAS) and enterprise ERP systems, enabling data interconnectivity and interoperability.