Improving urban-scale building occupancy and energy use estimation using a transportation-informed building occupancy estimation framework

Buildings consume a significant portion of global energy, highlighting the importance of accurately estimating building energy use for effective urban energy management. Building occupancy profiles are a key factor in physics-based building energy estimation. Traditional Urban Building Energy Modeling (UBEM) tools often rely on deterministic standard schedules, such as those provided by ASHRAE, which fail to account for spatial and temporal diversity in building occupancy and use a single profile for all buildings within the same use type, leading to inaccuracies in energy estimation. While novel data sources like WiFi and Bluetooth can generate building occupancy profiles, these methods are typically suitable only for generating typical occupancy profiles for a limited number of buildings, ignoring the impact of building geographical location on occupancy patterns. This paper introduces a novel approach, the Transportation-Informed Building Occupancy (TIBO) model, which leverages urban-scale transportation data—including metro, bus, bike-sharing, and vehicle flow data—to generate individualized building occupancy profiles. Our approach addresses the limitations of existing methods by incorporating extensive real-world spatial transportation data. We applied the TIBO model to estimate building occupancy profiles across different districts in Montreal and compared these profiles with ground truth data and those derived from ASHRAE models. Our results show that the TIBO model improves occupancy profile accuracy by 55.29–62.52 % compared to ASHRAE profiles in our case study. Additionally, integrating these more realistic TIBO profiles into UBEM improved electricity use demand estimation by an average of 2.03–78.66 % across various city zones relative to using ASHRAE profiles. This study introduces a novel integrated urban system that connects building energy modeling with transportation systems, facilitating cross-sector analysis. It further highlights how transportation and mobility patterns are effective in refining the accuracy of building energy use models.