4.7 Review

Urban energy use modeling methods and tools: A review and an outlook

期刊

BUILDING AND ENVIRONMENT
卷 161, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2019.106270

关键词

Urban energy use modeling; Operational energy; Transport energy; Embodied energy; Data-driven; Simulation

向作者/读者索取更多资源

Urban energy use modeling is important for understanding and managing energy performance in cities. However, the existing methods and tools have limitations in representing a realistic urban energy model and supporting energy performance evaluation at urban or neighborhood scales. In addition, there is a lack of an integrated approach for modeling and analyzing different components of urban energy use. The existing methods and tools for assessment of urban energy use often reduce the urban energy use definition to operational energy of buildings, ignoring other essential components such as transportation energy, and embodied energy of buildings and infrastructure. In addition, reliable and accurate urban energy prediction remains a challenge as methodological uncertainties that are embedded in the common methods are often not considered. This, in turn, affects the suitability of these approaches for decision-making purposes. The key limitation of data-driven methods stem from the use of aggregate data for energy use estimations and generalizing the status quo. In simulation-based methods, oversimplification of the urban context and failure to account for occupancy and human-related factors, and urban microclimate and inter-building effects are the major limitations. The present article provides a review of the current modeling methods, tools, and techniques in urban energy use modeling. It examines the strengths and limitations of each and presents an outlook for a future urban energy use modeling (UEUM) approach that could capture different components of urban energy use through a bottom-up hybrid data-driven and simulation-based techniques to build upon the strengths of the two methods while reducing the modeling uncertainties.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据