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Title: Development of city buildings dataset for urban building energy modeling

Abstract

Urban building energy modeling (UBEM) is becoming a proven tool to support energy efficiency programs for buildings in cities. Development of a city-scale dataset of the existing building stock is a critical step of UBEM to automatically generate energy models of urban buildings and simulate their performance. This study introduces data needs, data standards, and data sources to develop city building datasets for UBEM. First, a literature review of data needs for UBEM was conducted. Then, the capabilities of the current data standards for city building datasets were reviewed. Moreover, the existing public data sources from several pioneer cites were studied to evaluate whether they are adequate to support UBEM. The results show that most cities have adequate public data to support UBEM; however, the data are represented in different formats without standardization, and there is a lack of common keys to make the data mapping easier. Finally, a case study is presented to integrate the diverse data sources from multiple city departments of San Francisco. The data mapping process is introduced and discussed. It is recommended to use the unique building identifiers as the common keys in the data sources to simplify the data mapping process. Furthermore, the integrationmore » methods and workflow are applied to other U.S. cities for developing the city-scale datasets of their existing building stock, including San Jose, Los Angeles, and Boston.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. City of San Francisco, San Francisco, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1484220
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 183; Journal Issue: C; Journal ID: ISSN 0378-7788
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; City building dataset; CityGML; Urban building energy modeling; Data standards; Data mapping

Citation Formats

Chen, Yixing, Hong, Tianzhen, Luo, Xuan, and Hooper, Barry. Development of city buildings dataset for urban building energy modeling. United States: N. p., 2018. Web. doi:10.1016/j.enbuild.2018.11.008.
Chen, Yixing, Hong, Tianzhen, Luo, Xuan, & Hooper, Barry. Development of city buildings dataset for urban building energy modeling. United States. doi:10.1016/j.enbuild.2018.11.008.
Chen, Yixing, Hong, Tianzhen, Luo, Xuan, and Hooper, Barry. Sat . "Development of city buildings dataset for urban building energy modeling". United States. doi:10.1016/j.enbuild.2018.11.008.
@article{osti_1484220,
title = {Development of city buildings dataset for urban building energy modeling},
author = {Chen, Yixing and Hong, Tianzhen and Luo, Xuan and Hooper, Barry},
abstractNote = {Urban building energy modeling (UBEM) is becoming a proven tool to support energy efficiency programs for buildings in cities. Development of a city-scale dataset of the existing building stock is a critical step of UBEM to automatically generate energy models of urban buildings and simulate their performance. This study introduces data needs, data standards, and data sources to develop city building datasets for UBEM. First, a literature review of data needs for UBEM was conducted. Then, the capabilities of the current data standards for city building datasets were reviewed. Moreover, the existing public data sources from several pioneer cites were studied to evaluate whether they are adequate to support UBEM. The results show that most cities have adequate public data to support UBEM; however, the data are represented in different formats without standardization, and there is a lack of common keys to make the data mapping easier. Finally, a case study is presented to integrate the diverse data sources from multiple city departments of San Francisco. The data mapping process is introduced and discussed. It is recommended to use the unique building identifiers as the common keys in the data sources to simplify the data mapping process. Furthermore, the integration methods and workflow are applied to other U.S. cities for developing the city-scale datasets of their existing building stock, including San Jose, Los Angeles, and Boston.},
doi = {10.1016/j.enbuild.2018.11.008},
journal = {Energy and Buildings},
number = C,
volume = 183,
place = {United States},
year = {Sat Nov 17 00:00:00 EST 2018},
month = {Sat Nov 17 00:00:00 EST 2018}
}

Journal Article:
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