Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

2022 ◽  
Author(s):  
Zhang Deng ◽  
Yixing Chen ◽  
Jingjing Yang ◽  
Zhihua Chen
Keyword(s):  
Building Energy ◽  
Energy Modeling ◽  
Building Energy Modeling

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

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1049
Author(s):  
Zhang Deng ◽  
Yixing Chen ◽  
Xiao Pan ◽  
Zhiwen Peng ◽  
Jingjing Yang
Keyword(s):  
Large Scale ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Modeling ◽  
Office Buildings ◽  
Building Energy Modeling ◽  
Downtown Area ◽  
Point Of Interest

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.

Hybrid Dynamic-Empirical Building Energy Modeling Approach for an Existing Campus Building

2016 ◽  
Vol 22 (1) ◽  
pp. 04015010 ◽  
Author(s):  
William O. Collinge ◽  
Justin C. DeBlois ◽  
Amy E. Landis ◽  
Laura A. Schaefer ◽  
Melissa M. Bilec
Keyword(s):  
Building Energy ◽  
Energy Modeling ◽  
Building Energy Modeling ◽  
Modeling Approach ◽  
Campus Building

scholarly journals An open source analysis framework for large-scale building energy modeling

2020 ◽  
Vol 13 (5) ◽  
pp. 487-500
Author(s):  
Brian L. Ball ◽  
Nicholas Long ◽  
Katherine Fleming ◽  
Chris Balbach ◽  
Phylroy Lopez
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Building Energy ◽  
Energy Modeling ◽  
Source Analysis ◽  
Analysis Framework ◽  
Building Energy Modeling

Urban building energy modeling considering the heterogeneity of HVAC system stock: A case study on Japanese office building stock

2020 ◽  
Vol 207 ◽  
pp. 109590
Author(s):  
Bumjoon Kim ◽  
Yohei Yamaguchi ◽  
Shun Kimura ◽  
Yumei Ko ◽  
Kosuke Ikeda ◽  
...  
Keyword(s):  
Building Energy ◽  
Energy Modeling ◽  
Office Building ◽  
Hvac System ◽  
Building Energy Modeling ◽  
Building Stock

scholarly journals A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

2020 ◽  
Vol 279 ◽  
pp. 115834
Author(s):  
Usman Ali ◽  
Mohammad Haris Shamsi ◽  
Mark Bohacek ◽  
Karl Purcell ◽  
Cathal Hoare ◽  
...  
Keyword(s):  
Decision Making ◽  
Building Energy ◽  
Energy Modeling ◽  
Data Driven ◽  
Building Energy Modeling ◽  
Multi Scale ◽  
Data Driven Approach ◽  
Support Decision Making
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