@article{31312,
  keywords = {Urban Building Energy Model (UBEM), City building dataset, CityGML, Data standards, Data mapping},
  author = {Yixing Chen and Tianzhen Hong and Xuan Luo and Barry Hooper},
  title = {Development of city buildings dataset for urban building energy modeling},
  abstract = {<p>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. 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.</p>},
  year = {2019},
  journal = {Energy and Buildings},
  volume = {183},
  pages = {252 - 265},
  month = {11/2018},
  issn = {03787788},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818316852https://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/plain},
  doi = {10.1016/j.enbuild.2018.11.008},
  language = {eng},
}