I. Current Status and Challenges of Building HVAC Energy Consumption

Statistics show that HVAC systems account for 40%-60% of total energy consumption in commercial buildings, making them a top priority for building energy management. Traditional HVAC control relies mainly on manual experience and fixed schedules, making it difficult to dynamically adjust according to real-time environmental changes and actual usage needs, resulting in significant energy waste.

Common efficiency issues include:

• Overcooling/overheating: Temperature settings don't match actual needs
• Idle operation: Equipment runs at high load after occupants leave
• Improper fresh air volume: No linkage between CO₂ concentration and fresh air supply
• Hydraulic imbalance: Uneven cooling/heating distribution across zones

II. Sensor Data-Driven Intelligent Control Strategies

By deploying multi-type sensor networks to collect real-time environmental data and combining intelligent control algorithms, fine-grained energy efficiency management of HVAC systems can be achieved.

1. Temperature Sensors: Foundation of Precise Temperature Control

Deploy high-precision temperature sensors at air conditioning return air outlets, supply air outlets, and typical zones to monitor temperature distribution in real-time. Through PID algorithms, dynamically adjust valve openings and fan speeds to control temperature fluctuations within ±0.5℃, ensuring comfort while avoiding excessive cooling/heating.

2. CO₂ Sensors: On-Demand Fresh Air Supply

Indoor CO₂ concentration directly reflects occupancy density and air quality requirements. By linking CO₂ sensor data with fresh air units, "on-demand supply" is achieved: reducing fresh air volume when CO₂ is below 800ppm and increasing it when above 1000ppm, reducing fresh air processing energy consumption by 15%-25% while maintaining air quality.

3. Temperature and Humidity Coordinated Control

In high-humidity summer environments, appropriately raising temperature setpoints (e.g., 26℃→27℃) while enhancing dehumidification can reduce cooling load by 10%-15% without compromising thermal comfort. Humidity sensors make this strategy possible.

4. Occupancy Detection

Using infrared or millimeter-wave radar sensors to detect human presence in zones, enabling intelligent "turn on when occupied, setback when vacant" mode. Field measurements show this strategy can reduce office building HVAC energy consumption by 20%-30%.

III. System Architecture and Implementation Points

A complete sensor-driven HVAC energy optimization system includes:

1. Perception Layer: Multi-type sensors including temperature, humidity, CO₂, and occupancy detection
2. Transmission Layer: Communication networks such as Modbus/BACnet/LoRa
3. Platform Layer: Data collection, storage, analysis, and strategy distribution
4. Execution Layer: Variable frequency fans, electric valves, fresh air units, etc.

IV. Case Results

After implementing sensor-driven HVAC intelligent optimization in a 5A-grade office building:

• Annual HVAC energy consumption reduced by 32%
• Temperature complaints reduced by 78%
• Equipment maintenance costs reduced by 15%
• Investment payback period approximately 2.3 years

V. Conclusion

Sensor data serves as the "eyes" and "nerve endings" of HVAC energy efficiency optimization. Against the backdrop of carbon peaking and carbon neutrality goals, sensor-based intelligent control should no longer be exclusive to high-end projects but should become standard configuration for new buildings and energy retrofits.