Dynamic neural network

Demand-controlled ventilation has been proposed to improve indoor air quality and to save energy for ventilation. It is important to estimate occupancy in a building precisely in order to determine adequate ventilation airflow rates, especially when people are the major source of indoor contaminants such as in office buildings. In this paper, we investigate occupancy estimation methods using a dynamic neural network model based on carbon dioxide concentration in a space.