Bae, K. W., Choi, E. J., Choi, Y. J., Yun, J. Y., Yun, G. Y., Moon, H. J., & Moon, J. W. (2025). Real-time ventilation control for indoor CO2 management using occupant information. Building and Environment, 262, 113568. https://doi.org/10.1016/j.buildenv.2025.113568
Abstract
In contemporary Conventional ventilation control methods primarily rely on environmental measurements taken indoors but often overlook occupant-specific factors. This study presents an optimal ventilation control algorithm based on a real-time indoor CO2 concentration prediction model designed to enhance IAQ and energy efficiency. This model incorporates real-time occupant data, highlighting the considerable influence of occupant-related variables on the indoor CO2 levels. To this end, three deep learning architectures—deep neural networks, long short-term memory (LSTM), and gated recurrent units—were evaluated, with the LSTM model exhibiting superior accuracy and robustness. Using this model, a predictive ventilation control algorithm was developed to proactively regulate airflow and maintain CO2 concentrations below the recommended threshold of 1,000 ppm. The effectiveness of the proposed control strategy was validated using mockup experiments and living lab-based simulations. The results show that integrating real-time occupant data considerably enhances indoor comfort than rule-based ventilation control. Furthermore, optimal ventilation control resulted in a considerable decrease in energy consumption by approximately 24.74%, particularly in large-scale environments. These findings highlight the potential of the proposed method as a robust solution for next-generation indoor environmental management systems and intelligent control in smart buildings.
