The precision of energy simulations in buildings is crucial for efficiently managing and sustaining energy resources, particularly in intricate settings like university campuses. This study investigates how to enhance energy simulation models by integrating detailed occupancy data, evaluating stochastic occupancy models, and comparing the actual data of urban building archetypes. Traditional energy models, which often rely on standardized occupancy schedules, can lead to significant discrepancies between predicted and actual energy usage. This study introduces an approach incorporating electricity consumption data from campus facility management into building simulation models. This data reflects actual occupancy patterns, including peak and off-peak usage periods, transient populations, and space-specific activities, providing a more accurate simulation basis. The proposed model was applied to a group of buildings at Concordia University, Montreal, Canada. The results indicate a significant improvement in the model predictive accuracy, with the mean-variance reduced by 11%, highlighting the critical impact of accurately modeling occupancy patterns on energy consumption. This research underscores the importance of considering occupancy dynamics in building energy simulations. By aligning simulation parameters with real-world occupancy behaviors and applying them to urban building archetypes, it is possible to achieve more accurate energy conservation measures and sustainable practices. The findings demonstrate the potential for using existing infrastructure as scalable, cost-effective tools for collecting occupancy data, offering a novel, practical approach to enhancing simulation accuracy in various urban environments and promoting more sustainable, energy-efficient operations in campus settings.