Energy-efficient urban retrofits benefits from a novel demand-supply network with rapid urban building energy model and network analysis

Abstract Urban building energy modeling (UBEM) plays a crucial role in analyzing building energy use and has shown that large-scale UBEM can drive energy efficiency and sustainable development through urban retrofitting. However, large-scale UBEM presents challenges, including data acquisition workload, frequent parameter adjustments, and long simulation times. Moreover, the workflow connection between UBEM and urban retrofitting pathways remains unclear. Thus, this study proposes a framework that combines a fast, large-scale UBEM method in a Python environment with renewable energy integration to create energy demand-supply networks. The proposed UBEM method utilizes R-tree for geometric repairs, while EPPY efficiently batch-sets simulation parameters based on building function and performs batch simulations with EnergyPlus to quantify energy demand. Energy demand-supply networks are constructed through an improved gravitational model that considers location and functional mix, along with social network analysis. The framework was applied to Nanjing’s historic city center in Jiangsu, China, covering 23,279 buildings across 551 blocks with six functional categories, totaling 54,232,464 m 2 of building area. The energy use map reveals that high energy use intensity blocks (over 175 kWh/(m 2 ·year)) are distributed in the southern, particularly in commercial and old residential areas, while educational blocks have the highest photovoltaic (PV) potential. The simulation time using the multi-threaded EPPY method was only 14.1% of that with the conventional Ladybug tool for 75 buildings, and about 46.2% for ten urban blocks. Even with PV potential considered, 84.2% of blocks have energy demand exceeding supply, necessitating additional retrofitting. Combined retrofits are more effective than individual retrofits, achieving up to a 16.7% energy savings. This study provides new insights into large-scale UBEM and offers valuable decision-making support for energy-efficient urban retrofitting.