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Generating prototypical residential building geometry models using a new hybrid approach

Ma, Yuanli; Deng, Wu; Xie, Jing; Heath, Tim; Xiang, Yeyu; Hong, Yuanda

Generating prototypical residential building geometry models using a new hybrid approach Thumbnail


Authors

Yuanli Ma

Wu Deng

Jing Xie

Yeyu Xiang

Yuanda Hong



Abstract

Building prototyping has regularly been used in building performance analyses with statistically feasible models. The novelty of this research involves a new hybrid approach combining stratified sampling and k-means clustering to establish building geometry prototypes. The research focuses on residential buildings in Ningbo, China. Seventeen small residential districts (SRDs) containing 367 residential buildings were systemically selected for survey and data collection. The stratified sampling used building construction year as the main parameter to generate stratification. Floor numbers, shape coefficients, floor areas, and window-to-wall ratios were used as the four observations for k-means clustering. Based on this new approach, nine building geometry prototypes were identified and modelled. These statistically representative prototypes provide building geometrical information and characteristic-based evaluations for subsequent building performance analysis.

Journal Article Type Article
Acceptance Date Feb 8, 2021
Online Publication Date Apr 1, 2021
Publication Date Jan 1, 2022
Deposit Date Apr 28, 2021
Publicly Available Date Apr 2, 2022
Journal Building Simulation
Print ISSN 1996-3599
Electronic ISSN 1996-8744
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 15
Issue 1
Pages 17-28
DOI https://doi.org/10.1007/s12273-021-0779-6
Keywords Energy (miscellaneous); Building and Construction
Public URL https://nottingham-repository.worktribe.com/output/5500835
Publisher URL https://link.springer.com/article/10.1007/s12273-021-0779-6
Additional Information This is a post-peer-review, pre-copyedit version of an article published in Building Simulation. The final authenticated version is available online at: https://doi.org/10.1007/s12273-021-0779-6

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