Rapid urbanization has driven economic and social development, but it has also led to continued growth in building energy consumption. It is of great significance to ensure the user comfort while controlling the growth of building energy use. Accurate quantification of urban buildings' energy demand can support energy efficient and sustainable community design, assist urban morphology generation and optimization, building layout optimization, building shape and construction design, HVAC system optimization, assessment of the energy program and policy. In recent years, researchers worldwide have carried out research of urban scale energy consumption calculation methods from different perspectives, and encountered different technical difficulties. This paper provides a critical review on the energy modeling methods at urban neighborhood scale from the following three aspects: database, models and platforms. Through literature review, the authors indicate the advantages and limitations of current urban building energy calculation methods and tools, and propose the following possible approaches to improve the operational energy consumption calculation method for urban buildings: (1) develop micro-environment data generation methods that can be directly applied to energy consumption calculation of urban buildings; (2) improve the capabilities to collect, filter and convert the building information data by introducing the data mining technique; (3) introduce the cluster analysis and artificial intelligence technology to improve the speed of energy consumption calculation; (4) develop a visualization platform to realize real-time editing and calculating of urban design.

中文翻译：

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城市建筑运行能耗计算方法综述

快速的城市化推动了经济和社会发展，但也导致了建筑能耗的持续增长。在控制建筑能耗的增长的同时，确保用户的舒适度具有重要意义。准确量化城市建筑的能源需求可以支持高效节能和可持续的社区设计，协助城市形态生成和优化，建筑布局优化，建筑形状和建筑设计，HVAC系统优化，能源计划和政策评估。近年来，全球研究人员从不同角度进行了城市规模能耗计算方法的研究，遇到了不同的技术难题。本文从以下三个方面对城市邻里规模的能源建模方法进行了重要的回顾：数据库，模型和平台。通过文献综述，作者指出了当前城市建筑能耗计算方法和工具的优点和局限性，并提出了以下可能的方法来改进城市建筑能耗计算方法：（1）开发微环境数据生成方法，可直接应用于城市建筑能耗计算；（2）通过引入数据挖掘技术，提高建筑物信息数据的收集，过滤和转换能力；（3）引入聚类分析和人工智能技术，提高能耗计算速度；