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

Author

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  • Zhang Deng

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

  • Yixing Chen

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

  • Xiao Pan

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

  • Zhiwen Peng

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

  • Jingjing Yang

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

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.

Suggested Citation

  • Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1049-:d:500861
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    References listed on IDEAS

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    1. Ehsan Kamel, 2022. "A Systematic Literature Review of Physics-Based Urban Building Energy Modeling (UBEM) Tools, Data Sources, and Challenges for Energy Conservation," Energies, MDPI, vol. 15(22), pages 1-24, November.
    2. Constantinos A. Balaras & Andreas I. Theodoropoulos & Elena G. Dascalaki, 2023. "Geographic Information Systems for Facilitating Audits of the Urban Built Environment," Energies, MDPI, vol. 16(11), pages 1-26, May.
    3. Yixing Chen & Qilin Zhang & Zhang Deng & Xinran Fan & Zimu Xu & Xudong Kang & Kailing Pan & Zihao Guo, 2022. "Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    4. Yang, Jingjing & Deng, Zhang & Guo, Siyue & Chen, Yixing, 2023. "Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings," Applied Energy, Elsevier, vol. 331(C).

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