Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design

Abstract

Occupancy modeling plays a crucial role in building energy simulation in building design, yet studies investigating the influence of model types and their input assumptions remain sparse. This study evaluates the sensitivity of parameters and model robustness to uncertainties in parameter ranges and data availability during the design phase. We assessed three occupancy models—deterministic, probabilistic, and agent-based—using an open dataset with occupancy data across four space types. Our findings reveal that the predictive performance of deterministic and probabilistic models is influenced heavily by a few of the most sensitive parameters. In contrast, more parameters affected the prediction performance of the agent-based model. This suggests a need to prioritize sensitive parameter estimation to enhance prediction accuracy in deterministic and probabilistic models. Specifically, the occupancy density and start/end time, followed by schedule fractions, emerged as highly sensitive due to their significant impact on average occupancy profiles. We also found that for hourly energy predictions, narrowing the range of the top five sensitive parameters decreases the coefficient of variation of the root mean square error (CV(RMSE)) by 3% and reduces its standard deviation by half. These results underscore the importance of careful parameter estimation to improve the reliability of occupancy models in building energy simulations.