The residential sector accounts for an increasing amount of global energy use with continued urbanization. Residential energy-informed urban planning offers an economical and easy-to-operate approach to achieve more efficient urban energy utilization. However, quantifying the interactions between residential energy and urban planning remains an open challenge. This study proposes a holistic approach integrating GIS techniques, building energy modeling, and a global sensitivity analysis to prioritize eight key urban planning factors on the community energy performance based on a building energy dataset. The dataset, including urban planning and building information, was first established using GIS techniques and validated using survey data. The residential energy performance model at the community scale was developed using the clustering tree structure of residential building prototypes and building performance simulations. A combined data-driven and global sensitivity analysis approach was further applied to prioritize the impacts of eight vital urban planning factors on energy use intensity and peak load intensity. A case study of 1963 communities in Shanghai revealed that, for the energy performance of residential communities, the floor area ratio and building coverage ratio are the most influential factors, followed by the maximum height and high-rise proportion having a relatively low impact but higher than other factors. Overall, the proposed holistic approach generates robust insights into urban-scale residential energy performance, which can effectively inform urban planners to achieve more energy-efficient regulatory planning.

随着城市化的持续推进，住宅部门在全球能源使用量中的占比不断增加。以住宅能源为基础的城市规划提供了一种经济且易于操作的方法，以实现更有效的城市能源利用。然而，量化住宅能源和城市规划之间的相互作用仍然是一个开放的挑战。本研究提出了一种整合 GIS 技术、建筑能源建模和全球敏感性分析的整体方法，以基于建筑能源数据集对社区能源绩效的八个关键城市规划因素进行优先排序。该数据集，包括城市规划和建筑信息，首先使用 GIS 技术建立并使用调查数据进行验证。社区规模的住宅能源性能模型是使用住宅建筑原型的聚类树结构和建筑性能模拟开发的。进一步应用数据驱动和全球敏感性分析相结合的方法，优先考虑八个重要的城市规划因素对能源使用强度和峰值负荷强度的影响。对上海1963个社区的案例研究表明，对于住宅社区的能源绩效，容积率和建筑覆盖率是影响最大的因素，其次是最大高度和高层比例，影响相对较小，但影响较大。比其他因素。总体而言，所提出的整体方法对城市规模的住宅能源性能产生了深刻的见解，