Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets

被引:193
|
作者
Davila, Carlos Cerezo [1 ]
Reinhart, Christoph F. [1 ]
Bemis, Jamie L. [1 ]
机构
[1] MIT, Sustainable Design Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA
关键词
Urban modeling; Energy simulation; Building archetypes; Geospatial data; STOCHASTIC-MODEL; CONSUMPTION; PERFORMANCE; SIMULATION; NEIGHBORHOODS; METHODOLOGY; FRAMEWORK; IMPACT; STOCKS;
D O I
10.1016/j.energy.2016.10.057
中图分类号
O414.1 [热力学];
学科分类号
摘要
City governments and energy utilities are increasingly focusing on the development of energy efficiency strategies for buildings as a key component in emission reduction plans and energy supply strategies. To support these diverse needs, a new generation of Urban Building Energy Models (UBEM) is currently being developed and validated to estimate citywide hourly energy demands at the building level. However, in order for cities to rely on UBEMs, effective model generation and maintenance workflows are needed based on existing urban data structures. Within this context, the authors collaborated with the Boston Redevelopment Authority to develop a citywide UBEM based on official GIS datasets and a custom building archetype library. Energy models for 83,541 buildings were generated and assigned one of 52 use/age archetypes, within the CAD modelling environment Rhinoceros3D. The buildings were then simulated using the US DOE EnergyPlus simulation program, and results for buildings of the same archetype were crosschecked against data from the US national energy consumption surveys. A district-level intervention combining photovoltaics with demand side management is presented to demonstrate the ability of UBEM to provide actionable information. Lack of widely available archetype templates and metered energy data, were identified as key barriers within existing workflows that may impede cities from effectively applying UBEM to guide energy policy. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:237 / 250
页数:14
相关论文
共 31 条
  • [21] Automatic and rapid calibration of urban building energy models by learning from energy performance database
    Chen, Yixing
    Deng, Zhang
    Hong, Tianzhen
    APPLIED ENERGY, 2020, 277
  • [22] Community energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs)
    Rakha, Tarek
    El Kontar, Rawad
    ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2019, 46 (08) : 1517 - 1533
  • [23] How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling
    Bass, Brett
    New, Joshua
    Clinton, Nicholas
    Adams, Mark
    Copeland, Bill
    Amoo, Charles
    APPLIED ENERGY, 2022, 327
  • [24] Calibrated Urban Systems Design A Simulation-based Design Workflow using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs)
    Rakha, Tarek
    El Kontar, Rawad
    SMART AND HEALTHY WITHIN THE TWO-DEGREE LIMIT (PLEA 2018), VOL 1, 2018, : 2 - 7
  • [25] Facade geometry generation from low-resolution aerial photographs for building energy modeling
    Cao, Jun
    Metzmacher, Henning
    O'Donnell, James
    Frisch, Jerome
    Bazjanac, Vladimir
    Kobbelt, Leif
    van Treeck, Christoph
    BUILDING AND ENVIRONMENT, 2017, 123 : 601 - 624
  • [26] Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation
    Johari, Fatemeh
    Shadram, Farshid
    Widen, Joakim
    SUSTAINABLE CITIES AND SOCIETY, 2023, 96
  • [27] Generation and assessment of local climatic data from numerical meteorological codes for calibration of building energy models
    Silvero, Fabiana
    Lops, Camilla
    Montelpare, Sergio
    Rodrigues, Fernanda
    ENERGY AND BUILDINGS, 2019, 188 : 25 - 45
  • [28] Multi-objective optimal energy-efficient retrofit determination using hybrid urban building energy model: Considering uncertainties between models
    Linxi Luo
    Hailu Wei
    Ziqi Lin
    Jiyuan Wu
    Wei Wang
    Yongjun Sun
    Building Simulation, 2025, 18 (1) : 183 - 206
  • [29] Information Mining for Urban Building Energy Models (UBEMs) from Two Data Sources: OpenStreetMap and Baidu Map
    Wang, Chao
    Li, Yanxia
    Shi, Xing
    PROCEEDINGS OF BUILDING SIMULATION 2019: 16TH CONFERENCE OF IBPSA, 2020, : 3369 - 3376
  • [30] Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling
    Wurm, Michael
    Droin, Ariane
    Stark, Thomas
    Geiss, Christian
    Sulzer, Wolfgang
    Taubenboeck, Hannes
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (01)