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Research Article | Open Access

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

Fengmin Su1Yu Zhu1Jinghan Pan1Linxi Luo1Rui Jing2Qinran Hu3Wei Wang1,4( )Yongjun Sun5( )
School of Architecture, Southeast University, Nanjing, Jiangsu Province, 210096, China
College of Energy, Xiamen University, Xiamen, Fujian Province, 361005, China
School of Electrical Engineering, Southeast University, Nanjing, Jiangsu Province, 210096, China
Key Laboratory of Urban and Architectural Heritage Conservation (Southeast University), Ministry of Education, China
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
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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 m2 of building area. The energy use map reveals that high energy use intensity blocks (over 175 kWh/(m2·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.

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Building Simulation
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Cite this article:
Su F, Zhu Y, Pan J, et al. Energy-efficient urban retrofits benefits from a novel demand-supply network with rapid urban building energy model and network analysis. Building Simulation, 2025, 18(10): 2657-2676. https://doi.org/10.1007/s12273-025-1326-7

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Received: 14 April 2025
Revised: 14 June 2025
Accepted: 01 July 2025
Published: 19 September 2025
© The Author(s) 2025

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