Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. However, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces. First, the coupled CFD model is developed to study wind-driven cross ventilation in high-density cities. A 3D isothermal CFD simulation is solved using the RNG k-ε turbulent model. The simulation results are compared with wind tunnel experiment data from literature. Second, six key design variables are used to generate 3840 parametric design variations for natural ventilation assessment. Third, a novel integrated index CIOIv (coupled indoor and outdoor interaction) is proposed to evaluate the wind speed ratio between the indoor area and outdoor reference area. For demonstration, CIOIv,F1 is used to represent CIOIv on the ground level. Data-driven CIOIv,F1 models are developed to predict indoor building ventilation potential for quick early design support. Compared with multivariate linear regression model, the Gradient Boosting non-linear model displays much higher prediction accuracy (mean absolute percentage error = 0.16 with R2 = 0.8). In early design stage, designers and engineers can skip the computational expensive CFD simulations and use this data-drive model to quickly check the building natural ventilation potentials of different design options in an urban environment.

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.