CO₂- and PM_2.5-Focused Optimal Ventilation Strategy Based on Predictive Control

This study developed and evaluated an optimal ventilation strategy for variable air volume (VAV) systems, targeting carbon dioxide (CO₂) and particulate matter less than 2.5 μm in diameter (PM_2.5) concentrations. The strategy integrates system-level demand-controlled ventilation (DCV) based on real-time occupancy data and zone-level predictive control using indoor air quality (IAQ) prediction models. By predicting indoor CO₂ and PM_2.5 levels for the subsequent time step and dynamically adjusting control priorities, optimal airflow is determined. A co-simulation model integrating EnergyPlus, CONTAM, and Python was employed for model training and testing. The proposed strategy was compared with on–off control, CO₂ predictive control, and PM_2.5 predictive control, demonstrating superior prediction accuracy and stable IAQ maintenance. The optimal ventilation strategy achieved the highest performance, maintaining CO₂ and PM_2.5 levels below their respective upper limits of 100% and 97.33% of the time. Although this strategy resulted in slightly higher energy consumption compared to the other control algorithms due to its multivariable control approach, it effectively maintained IAQ standards. This method simplifies development and maintenance by circumventing the need for complex optimization, providing a flexible and cost-effective solution for IAQ management. Future research will focus on developing integrated VAV system control strategies that ensure comfort year-round, addressing both energy efficiency and thermal comfort.