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

被引:110
作者
Abbasabadi, Narjes [1 ]
Ashayeri, J. K. Mehdi [1 ]
机构
[1] IIT, Coll Architecture, 3360 S State St, Chicago, IL 60616 USA
关键词
Urban energy use modeling; Operational energy; Transport energy; Embodied energy; Data-driven; Simulation; EXTREME LEARNING-MACHINE; DATA-DRIVEN; EMBODIED ENERGY; NEURAL-NETWORKS; CONSUMPTION; SIMULATION; BUILDINGS; CITY; SYSTEM; OCCUPANCY;
D O I
10.1016/j.buildenv.2019.106270
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 139 条
  • [41] Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts
    Fonseca, Jimeno A.
    Schlueter, Arno
    [J]. APPLIED ENERGY, 2015, 142 : 247 - 265
  • [42] A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm
    Gao, Xuefeng
    Malkawi, Ali
    [J]. ENERGY AND BUILDINGS, 2014, 84 : 607 - 616
  • [43] Linking energy and transport models to support policy making
    Gerboni, Raffaella
    Grosso, Daniele
    Carpignano, Andrea
    Chiara, Bruno Dalla
    [J]. ENERGY POLICY, 2017, 111 : 336 - 345
  • [44] Harnessing buildings' operational diversity in a computational framework for high-resolution urban energy modeling
    Ghiassi, Neda
    Tahmasebi, Farhang
    Mahdavi, Ardeshir
    [J]. BUILDING SIMULATION, 2017, 10 (06) : 1005 - 1021
  • [45] Guy S., 2000, The Sociology of Energy, Buildings and the Environment: Constructing Knowledge, Designing Practice, DOI DOI 10.4324/9781315812373
  • [46] A review on occupant behavior in urban building energy models
    Happle, Gabriel
    Fonseca, Jimeno A.
    Schlueter, Arno
    [J]. ENERGY AND BUILDINGS, 2018, 174 : 276 - 292
  • [47] Demonstration of reduced-order urban scale building energy models
    Heidarinejad, Mohammad
    Mattise, Nicholas
    Dahlhausen, Matthew
    Sharma, Krishang
    Benne, Kyle
    Macumber, Daniel
    Brackney, Larry
    Srebric, Jelena
    [J]. ENERGY AND BUILDINGS, 2017, 156 : 17 - 28
  • [48] Improved benchmarking comparability for energy consumption in schools
    Hong, Sung-Min
    Paterson, Greig
    Mumovic, Dejan
    Steadman, Philip
    [J]. BUILDING RESEARCH AND INFORMATION, 2014, 42 (01) : 47 - 61
  • [49] Spatial distribution of urban building energy consumption by end use
    Howard, B.
    Parshall, L.
    Thompson, J.
    Hammer, S.
    Dickinson, J.
    Modi, V.
    [J]. ENERGY AND BUILDINGS, 2012, 45 : 141 - 151
  • [50] Identifying key variables and interactions in statistical models of building energy consumption using regularization
    Hsu, David
    [J]. ENERGY, 2015, 83 : 144 - 155