Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling

Zhang Deng, Yixing Chen, Xiao Pan, Zhiwen Peng and Jingjing Yang
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Zhang Deng: College of Civil Engineering, Hunan University, Changsha 410082, China
Yixing Chen: College of Civil Engineering, Hunan University, Changsha 410082, China
Xiao Pan: College of Civil Engineering, Hunan University, Changsha 410082, China
Zhiwen Peng: College of Civil Engineering, Hunan University, Changsha 410082, China
Jingjing Yang: College of Civil Engineering, Hunan University, Changsha 410082, China

Energies, 2021, vol. 14, issue 4, 1-17

Abstract: Urban building energy modeling (UBEM) is arousing interest in building energy modeling, which requires a large building dataset as an input. Building use is a critical parameter to infer archetype buildings for UBEM. This paper presented a case study to determine building use for city-scale buildings by integrating the Geographic Information System (GIS) based point-of-interest (POI) and community boundary datasets. A total of 68,966 building footprints, 281,767 POI data, and 3367 community boundaries were collected for Changsha, China. The primary building use was determined when a building was inside a community boundary (i.e., hospital or residential boundary) or the building contained POI data with main attributes (i.e., hotel or office building). Clustering analysis was used to divide buildings into sub-types for better energy performance evaluation. The method successfully identified building uses for 47,428 buildings among 68,966 building footprints, including 34,401 residential buildings, 1039 office buildings, 141 shopping malls, and 932 hotels. A validation process was carried out for 7895 buildings in the downtown area, which showed an overall accuracy rate of 86%. A UBEM case study for 243 office buildings in the downtown area was developed with the information identified from the POI and community boundary datasets. The proposed building use determination method can be easily applied to other cities. We will integrate the historical aerial imagery to determine the year of construction for a large scale of buildings in the future.

Keywords: urban building energy modeling; building use; point of interest; community boundary; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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