Data-driven modelling of pressurized corridor ventilation system performance in a multi-unit residential building.

Abstract

Pressurized corridor (PC) ventilation systems are a common method used in existing multi-unit residential buildings (MURBs) to deliver make-up air to individual units, and as a means of controlling inter-zonal odour/contaminant transfer. In PC systems, ventilation air is supplied directly to the common corridor and enters the units via intentional undercuts at the unit entry doors. In practice, the amount of ventilation air supplied to each unit is dependent on the air pressure differential between the two zones, which can be affected by occupant behaviours, such as window and unit exhaust fan operation; wind; or large indoor-outdoor temperature differentials. Accurately characterizing the impact of these variables on building pressure differentials is critical to not only identifying conditions when depressurization events may occur (which would result in a lack of ventilation to dwelling unit and the potential for contaminant movement from units to the corridor), but also understanding how operational changes can improve system operation. This paper will describe the development of an XGBoost regression model for predicting inter-zonal pressure differentials in a contemporary MURB with a PC system. The model was trained and validated using measurements collected as part of a 6-month field study in a 17-storey MURB located in Toronto, Canada, including corridor-to-unit and exterior-to-unit differential pressures, window/door operation, corridor supply air flow rates and interior/exterior temperature and relative humidity. Unit exhaust fan operation was inferred from the unit differential pressure data. This paper addresses feature selection, hyperparameter tuning and accuracy assessment, with a specific emphasis on evaluating the potential for the use of the model as a diagnostic tool and testing environment to evaluate ventilation system performance in multi-unit residential buildings.