A method for zone-level urban building energy modeling in data-scarce built environments

Urban Building Energy Modeling (UBEM) is critical for improving the resilience of cities to climate change, but most regions lack of data sets necessary for its development. A bottom-up approach is a viable method to initiate comprehensive UBEM frameworks. However, this process is often challenged by incomplete data, which can significantly affect the reliability and resolution of simulation results. Traditional deterministic approaches commonly used in UBEM fail to capture the diversity of the building stock. Thus, probabilistic methods are increasingly used, which require a careful examination of the types and patterns of missing data. This paper fills a critical gap in the literature by presenting a probabilistic approach to data generation for data-scarce environments to build high-resolution bottom-up urban-scale models while preserving building stock heterogeneity and statistical consistency. Our methodology includes advanced data imputation and generation techniques based on density estimations. This approach is illustrated with a case study in the Bah & ccedil;elievler neighborhood in Ankara, Turkey. We have developed four different UBEM versions with varying degrees of data granularity to demonstrate the effectiveness of our methods. The proposed models incorporate comprehensive data on construction and occupant-related parameters, enhancing the resolution of energy simulations for buildings. This research provides a robust framework for the development of UBEM in regions lacking comprehensive datasets, ultimately supporting informed policy making and improved urban energy management.