Development of an Indoor CO2 Prediction Model and Optimal Ventilation Control Algorithm Based on an Occupant Personal Information

Purpose: This study aimed to develop a CO₂ prediction model to enhance indoor air quality (IAQ) management by incorporating real-time occupant-specific characteristics such as activity levels (MET), gender, and BMI. Traditional models typically rely on basic variables like occupant count, often ignoring individual factors that significantly affect CO₂ emissions. By including these variables, the model seeks to improve predictive accuracy and optimize ventilation control systems for enhanced energy efficiency and IAQ management. Method: Two predictive models were developed using DNN and GRU machine learning algorithms. One model utilized basic number of occupants and environmental (outdoor CO₂ , indoor CO₂ , ventilation system flowrate) data, while the enhanced model incorporated MET, gender, and BMI. Both models were trained on mock-up data collected from a controlled environment, including CO₂ concentration, ventilation flow rates, and occupant information. Their performance was evaluated using MAE, CvRMSE, and R². Result: The enhanced model, integrating occupant-specific variables, demonstrated significant improvements in prediction accuracy compared to the traditional model, with MAE and CvRMSE values improving by 3.93% and 6.92%, respectively. These results highlight the importance of detailed occupant data for real-time IAQ management and the potential for greater efficiency in ventilation control and energy savings in sustainable buildings.