A novel Monte Carlo modelling method to support control strategies development in building ventilation

Ventilation is critical for maintaining thermal comfort and air quality in buildings. However, developing ventilation control is challenging due to the large number of control variables and performance criteria. Typical ventilation controls are On-Off controls, time schedules, and PI/PID controls. Specific parameters are tuned based on simple rules of thumb and the engineer’s experience. Although building simulation tools are commonly applied, they are normally used to evaluate the performance of certain control strategies rather than guide the development of these control strategies. This study presents a novel Monte Carlo modelling method that supports the early-stage development of ventilation control. The method consists of the following steps: (1) Creating an initial building model, (2) Identifying relevant control variables and assigning probability distributions, (3) Executing Monte Carlo simulations, (4a) Applying filters to assess the outcomes, (4b) Performing sensitivity analysis on control variables, (5) Selecting a ventilation control strategy fulfilling control objectives. The method is tested on a classroom equipped with a hybrid ventilation system. The case study demonstrates that the novel approach, allows ventilation designers to systematically identify high-performance control solutions for multiple control variables and performance requirements. Thus, offering clear advantages over the traditional trial-and-error method.