Smarter Cooling, Lower Energy
An intelligent control brain for central chiller plants. By learning from operational data, it dynamically adjusts chillers, pumps, and cooling towers to maintain comfort while cutting energy use and carbon emissions.
The Intelligence Loop
A closed-loop 'Perception – Prediction – Decision – Optimization' model that continuously fine-tunes operation to achieve peak system efficiency.
Perception
Real-time data collection & load monitoring
Prediction
Forecasting weather & equipment performance
Decision
Calculating optimal staging & setpoints
Optimization
Automated execution & feedback
Deep Energy-Saving Mechanism
Intelligent coordination delivers 8–10% total plant energy savings by targeting the three core components.
Chillers
Neural network optimization based on COP & load rate.
Optimizes outlet temp (+1–2℃)
Water Pumps
AI frequency optimization + VFD control.
Eliminates 'small temp difference' syndrome
Cooling Towers
Joint optimization of fan & pump efficiency.
Lowers approach temp (-1°C)
AI-Driven Small Models
Simulates performance using minute-level data to recommend precise operational settings.
Self-Learning Architecture
Combines neural networks and expert rules, improving accuracy automatically over time.
Plug-and-Play Integration
Seamlessly compatible with existing BMS and IoT protocols (BACnet, Modbus, MQTT).
Quantifiable Results
Transforms traditional systems into intelligent networks, delivering measurable electricity cost reductions.
System Architecture Diagram
Proven Results
Fresenius Kabi (Phase 4): System efficiency improved from 4.21 to 4.83 within just four months.
Fresenius Kabi (Pharma): Two chiller plants saved ~RMB 2M annually in electricity costs.
Shanghai Hines 1MP: Achieved immediate savings during the initial AI deployment phase.
Metro China: IoT integration enabled 100% paperless operation and optimized maintenance staffing.