At 3 a.m. in a semiconductor factory, a cold pump in the HVAC plant room suddenly emitted an abnormal hum. The duty engineer's phone instantly received an intelligent anomaly alert: "Bearing wear detected on Cold Pump No. 3. Recommend switching to backup unit. Estimated resolution time: 8 minutes." Two years prior, an identical failure had paralyzed the production line for 12 hours, causing losses exceeding one million RMB. This efficient, autonomous late-night rescue operation was powered by GETECH's Energy and Carbon Facility Digitalization Solution, centered on AI-driven HVAC optimization—as if endowing equipment with an "autonomous nervous reflex." This very solution recently secured first prize in the Intelligent Manufacturing track at the 2025 "Data Elements X" competition Hubei Division finals.

It is well-known that HVAC plant systems account for nearly half of the typical semiconductor factory's auxiliary energy consumption. Comprising chillers, water pumps, cooling towers, and piping networks, these systems operate in coordination to produce chilled or hot water, which is then delivered to the cleanroom to maintain a constant temperature, humidity, and dust-free environment. For many facility engineers, managing the HVAC system resembles fumbling for a light switch in a dark room—multiple pain points are evident, yet solutions remain elusive:

Unperceived Energy Black Holes: Manual inspections are time-consuming and labor-intensive, energy consumption data reporting is lagging, and deficient equipment management leads to unnoticed waste.

Unpredictable Sudden Failures: Fault response is delayed, operational conditions cannot be pinpointed accurately, and maintenance scheduling is often untimely, disrupting production plans and schedules.

Decision-less Energy Consumption Fog: Energy accounting is unclear, responsibility is difficult to assign, unified standards are absent, multi-departmental roles are blurred, making energy waste hard to trace and attribute.

Unsubstantiated Operational Strategies: Equipment operation relies on manual experience, missing optimal decisions; absent data and information delays prevent continuous energy consumption optimization.

Addressing these core industry pain points—low management efficiency, high equipment idling rates, inefficient fault response, and limited access to energy optimization and operational strategy tuning—GETECH developed the AI HVAC Intelligent Control High-Efficiency Plant Control Platform. The platform connects directly to plant room equipment or PLC control cabinets, enabling signal acquisition and data interaction. Leveraging real-time equipment operational data, characteristics, and status, combined with AI models and mechanistic algorithms, it simulates and computes the optimal operational strategy for the plant room and issues real-time commands to achieve peak efficiency. The platform incorporates dozens of functions including real-time energy efficiency monitoring, dynamic remote control, data analysis, operational algorithm optimization, online energy efficiency simulation, AI control optimization, and demand load prediction, delivering tangible energy-saving benefits where "every saved kilowatt-hour is reflected on the electricity bill."

To date, GETECH's Energy and Carbon Facility Digitalization Solution, centered on AI HVAC optimization, has served over 100 manufacturing factories, cumulatively reducing energy consumption costs by over RMB 200 million, achieving a 15%-20% annual reduction in comprehensive energy consumption, a 50% improvement in management efficiency, and establishing itself as a benchmark solution for industrial green transformation.

Key Value

01 Algorithmic Energy Saving

Utilizes equipment performance models and optimization algorithms to automatically find and maintain the system's optimal operating point.

02 Holistic Energy Saving

Conducts integrated optimization for both the plant room and end-use equipment, uncovering greater energy-saving potential factory-wide.

03 Full-Cycle Verification

Employs a fair, clear energy-saving measurement methodology within the cockpit interface; energy-saving results are directly verified against electricity bills.

04 Accompanied Operation & Maintenance

Provides full life-cycle accompanied O&M, ensuring sustained energy savings while continuously identifying further energy-saving opportunities for the customer.

Main Features

The platform offers capabilities for monitoring, control, reporting, analysis, predictive diagnostics, and AI-driven energy-saving optimization. It encompasses core BAS/FMCS system functionalities while significantly enhancing the flexibility and intelligence of HVAC optimization.

01 Visualized Energy Usage Monitoring

Intuitively displays real-time operational status and key parameters for systems and equipment.

02 AI Predictive Diagnostics

AI algorithms enable predictive fault diagnosis and provide actionable resolution recommendations.

03 AI Strategic Energy-Saving Control

AI optimization algorithms facilitate automatic control, achieving high efficiency, energy savings, and unmanned operation.

Additionally, it covers key functions like an annual energy efficiency cockpit, data analysis and report management, an energy efficiency calendar, and one-click adjustments, helping customers realize a new HVAC optimization experience characterized by a visible power meter, a calculable carbon account, and understandable AI.

Core Advantages

01 Comprehensive Features, Low Cost, Fast Delivery, High Usability

Includes essential BAS/FMCS functions (point management, configuration, alarms, reports) and combines expert experience with AI algorithms to deliver active optimization, unmanned operation, and safe maintenance for high-efficiency plants, with a dedicated focus on HVAC system energy-saving control.

02 Broad Compatibility with Equipment Brands, Wide Application Scenarios

Features a self-developed multi-protocol adaptation engine with high versatility and compatibility, supporting equipment from mainstream manufacturers like Siemens, Trane, and McQuay. Compatibility with diverse protocols enables extensive application scenarios. The engine supports over 95% of common industrial protocols, enabling plug-and-play equipment integration and eliminating protocol isolation issues.

03 Advanced Algorithm Capabilities & Automated Integration

Integrates LSTM load forecasting and dynamic optimization algorithms. Performs system modeling based on physical laws for the entire system (water side + air side), delivers professional, comprehensive chiller performance prediction, enables 8760-hour system simulation, and executes real-time dynamic identification and algorithm correction.

04 Verifiable Energy Savings: Every Saved kWh Visible on the Bill

Through energy usage profile diagnosis and closed-loop execution of AI strategies, the system guarantees measurable and verifiable energy savings.

Industry Practice

Following the implementation of GETECH's comprehensive, digital, intelligent facility management system across all processes for a leading pan-semiconductor customer, the client utilized functionalities including Energy Management (EMS), Carbon Management (CMS), Equipment Health Management (EHM), Environment, Health & Safety (EHS), and AI applications. This resulted in a 50% increase in management efficiency, a reduction of over RMB 200 million in group comprehensive energy consumption costs, and cumulative CO2 emission reductions exceeding 280,000 tons.

A critical application was HVAC AI optimization. Targeting the significant energy consumption of the HVAC system, AI algorithm models and mechanistic models, combined with cloud computing and IoT technologies, enabled precise intelligent control, real-time feedback, energy consumption prediction and simulation, and automatic algorithm optimization strategy operation. This empowered the enterprise to achieve smart, efficient operation of its HVAC system and significantly reduce overall energy consumption costs.

Looking ahead, GETECH will continue to leverage advanced Energy and Carbon Facility Digitalization Solutions to redefine the balance between manufacturing productivity and sustainable development, embedding a "low-carbon DNA" into more customer factories, exceeding the milestone of 350,000 tons of carbon reduction, and accelerating the green transformation of advanced manufacturing.