Demand-Controlled Ventilation AI
AI design of demand-controlled ventilation systems for energy efficiency.
Definition
Demand-Controlled Ventilation AI designs HVAC systems that adjust outdoor air based on actual occupancy. It optimizes CO2 sensor placement, controls strategies, and system sizing to reduce energy consumption while maintaining indoor air quality in variable occupancy spaces.
In Depth
Demand-controlled ventilation (DCV) adjusts the outdoor air supply based on actual occupancy rather than the design maximum, reducing the energy penalty of conditioning outdoor air when spaces are partially occupied. AI optimizes the DCV design by selecting sensor types, locations, and control algorithms that achieve energy savings while maintaining air quality.
CO2 sensing is the standard DCV technology for office and commercial spaces, but the sensor placement and setpoint selection significantly affect both energy savings and air quality. AI determines optimal sensor locations based on the room geometry and airflow patterns, and sets CO2 concentration limits that correspond to the ventilation rates required by ASHRAE 62.1 for each space type.
Examples
Designing DCV systems
Optimizing sensor placement
Reducing ventilation energy
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Demand-Controlled Ventilation AI designs HVAC systems that adjust outdoor air based on actual occupancy. It optimizes CO2 sensor placement, controls strategies, and system sizing to reduce energy consumption while maintaining indoor air quality in variable occupancy spaces.
Designing DCV systems. Optimizing sensor placement. Reducing ventilation energy.
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