Building Ventilation Optimization Through Occupant-Centered Computer Vision Analysis

Buildings consume about 40 % of all energy. Ventilation plays a significant role in both the energy consumption of buildings and the comfort of occupants. To achieve energy efficiency and comfort, smarter ventilation control algorithms can be employed, such as those with feedback based on CO₂ levels. Furthermore, by knowing the current number of people in a space, ventilation can theoretically be adjusted to maintain a constant CO₂ level without wasting energy when people are not present. An additional benefit of such control could arise due to occupants’ habits. For example, if a person senses elevated CO₂ levels, even if the ventilation system has started operating more intense, they might choose to open a window, potentially compromising energy efficiency. Therefore, if the control algorithm were to maintain a constant CO₂ level, occupants may be less likely to open windows.

In our work, we explore a model in combination with a custom monitoring system based on computer vision to implement such control. The monitoring system combines outside and inside CO₂ sensors with precise people counting based on computer vision to provide data to the model. The model relies on the mass balance equation for CO₂ and considers the historical data of the number of occupants and their activities to estimate the overall CO₂ generation in indoor spaces. The results suggest that the model can effectively forecast CO₂ dynamics with an absolute deviation of 40 ppm. However, it was observed that the analysis of the actual air exchange level could be compromised by several factors.