Modeling occupants’ behaviour to improve the building performance simulation of classrooms

Window operation in naturally ventilated classrooms is the only strategy for achieving proper air change rates. The modeling of the ventilation rate based on the window state implies knowledge of the window opening angle to evaluate the net exchange area. Nonetheless, the sensors most used to monitor window opening state are contact sensors, which allow only a binary state (i.e., open/close) to be devised. This work aims to investigate the effect that window opening information has on ventilation rates and building performance simulation by comparing the case in which window opening is described by the opening angle to the condition in which it is described as a binary I/O variable. A measurement campaign was conducted on six classrooms in a secondary school in Morlupo, Rome. Temperature, CO2 concentration and relative humidity were monitored in the six classes, while temperature and relative humidity were monitored outdoors. During school time, a few students per class were asked to report information concerning the number of occupants and the opening state of windows and shutters on a discrete scale. From the data collected, an equivalent opening area was calculated, accounting for the combined opening of windows and shutters, being therefore representative of the net exchange area. Based on the original dataset, a second dataset was generated by considering binary window opening information both for windows and shutters. The two datasets were used, together with environmental data, to train behavioral models that were then fed into a building energy simulation model. The results of the simulations show that the simplified dataset causes an overestimation of the air changes and of the building energy need.