Estimation of time-varying air exchange rate using occupant-generated CO₂

This study introduces a novel method for estimating the time-varying air exchange rate (AER) in indoor environments by utilizing CO₂ generated by occupants. Accurately estimating the AER is critical for understanding the building energy performance, as it significantly affects the heating, cooling, and ventilation loads. Traditional AER models often assume a static value that fails to account for real-time variations influenced by factors such as occupant behavior, environmental conditions, and ventilation strategies. The proposed sliding window approach segments time-series data, identifies changes in ventilation modes, and optimizes AER estimation. This method was validated through field measurements conducted over a 12-week period in a university classroom, demonstrating its accuracy and practical applicability. The predicted AER was validated through indirect and direct comparisons, thereby confirming the reliability of the estimation process. This method also enables the estimation of occupant numbers based on CO₂ emissions, further enhancing its utility in real-world applications. This method holds the potential for application in building energy simulations, particularly in scenarios involving demand-controlled ventilation and indoor air quality management systems that require real-time AER adjustments. Additionally, this method can be integrated into smart building management systems to optimize energy consumption and enhance occupant comfort based on real-time conditions.