14 JAN 2025 (TUE) 15:30-15:50

From City to Nation: Urban Building Energy Modelling at High Spatiotemporal Resolution 

Ms LIU Qinqin 

( Supervisor: Prof Yuyu Zhou )

Abstract:

Urbanization is a well-documented global phenomenon that poses significant challenges in terms of energy demand and environmental sustainability. Urban areas account for 75% of global primary energy consumption, with buildings contributing to 30–70% of a city’s total primary energy use. This substantial energy consumption in the building sector not only exacerbates climate change but also raises concerns about the sustainable utilization of energy and natural resources. Urban building energy modelling offers a crucial pathway to address these challenges by identifying opportunities for energy efficiency. However, accurately quantifying building energy consumption at high spatial and temporal resolutions remains challenging on a large scale due to modelling complexity and data scarcity. These limitations have hindered comprehensive research on urban building energy use and its interactions with climate. 

To overcome these limitations above and provide stakeholders with quantitative insights for informed decision-making in urban energy planning and large-scale building energy retrofits. This study aims to model urban building energy use at the single-building and hourly levels for city- and national-scale applications. The primary objectives of this research are threefold: (1) Development of a GIS-Based City-Scale Building Energy Model (GIS-CBEM): This model integrates physical urban building energy model with geographical information systems (GIS) to simulate city-scale building energy consumption with high spatial and temporal resolution. The analysis encompasses energy use profiles for various building types and provides detailed insights into city-scale energy dynamics at diverse spatial and temporal scales. (2) Quantification of the Urban Heat Island (UHI) Effect on Building Energy Use: This component investigates the influence of the UHI effect on monthly cooling energy demand, as well as cooling energy use under extreme heat conditions and during peak load periods. (3) Upscaling the Urban Building Energy Model to the National Level: Utilizing machine learning and transfer learning techniques, this study will extend the urban energy model to a national scale, with a specific focus on Chinese cities. This part will quantify building energy use at an hourly and single-building level, unveiling energy use characteristics by building type, end use, and energy source across different urban contexts in China. 

This study offers a novel and comprehensive approach to addressing the challenges of building energy modelling across urban and national scales. By integrating GIS-based physical modelling with advanced machine learning techniques, it enables high-resolution simulations at single-building and hourly levels, providing critical insights into energy use dynamics. The results not only enable stakeholders to optimize energy efficiency and sustainability in urban areas but also offer a transferable framework for applying high-resolution modelling techniques to other cities and countries. This work thus supports global efforts toward sustainable energy use and climate resilience in the built environment.