Abstract & Overview
This study investigates the performance of different demand-controlled ventilation strategies for improving indoor air quality while optimizing energy efficiency. The experimental research was conducted at the Indoor Live Lab at the University of Coimbra using a smart window equipped with mechanical ventilation boxes, occupancy sensors, and a real-time CO2 monitoring system. Several occupancy-based and CO2-based ventilation control strategies were implemented and tested to dynamically adjust ventilation rates according to real-time indoor conditions, including (1) occupancy period-based control, (2) occupancy level-based control, (3) ON-OFF CO₂-based control, (4) multi-level CO₂-based control, and (5) modulating CO₂-based control. The results indicate that intelligent control strategies can significantly reduce energy consumption while maintaining indoor air quality within acceptable limits. Among the CO₂-based controls, strategy 5 achieved optimal performance, reducing energy consumption by 60% compared to the simple ON-OFF strategy, while maintaining satisfactory indoor air quality. Regarding occupancy-based strategies, strategy 2 showed 58% energy savings compared to the simple occupancy period-based control, but with greater CO₂ concentration fluctuation. The results demonstrate that intelligent DCV systems can simultaneously reduce ventilation energy use by 60% and maintain compliant indoor air quality levels, with modulating CO₂-based control proving most effective. The findings highlight the potential of integrating sensor-based ventilation controls in office spaces to achieve energy savings, enhance occupant comfort, and contribute to the development of smarter, more sustainable buildings. Future research should explore the integration of predictive analytics and multi-pollutant sensing to further optimize demand-controlled ventilation performance.