eprintid: 10128881
rev_number: 17
eprint_status: archive
userid: 608
dir: disk0/10/12/88/81
datestamp: 2021-06-02 11:14:44
lastmod: 2022-07-08 10:02:59
status_changed: 2021-06-02 11:14:44
type: article
metadata_visibility: show
creators_name: Tian, Z
creators_name: Zhang, X
creators_name: Wei, S
creators_name: Du, S
creators_name: Shi, X
title: A review of data-driven building performance analysis and design on big on-site building performance data
ispublished: pub
divisions: UCL
divisions: B04
divisions: C04
divisions: F37
keywords: Building performance design, data-driven, building energy, building performance data
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Building performance design (BPD) is a crucial pathway to achieve high-performance buildings. Previous simulation-based BPD is being questioned due to the performance gaps between simulated and measured values. In recent years, accumulated on-site building performance data (OBPD) make it possible to analyze and design buildings with data-driven methods. This article makes a review of previous studies that conducted data-driven building performance analysis and design on a large amount of OBPD. The covered studies are summarized by the applied techniques, i.e., statistics, regression, classification, and clustering. The data used by these studies are compared and discussed emphasizing the data size and public availability. A comprehensive discussion is given about the achievements of existing studies, and challenges for boosting data-driven BPD from three aspects, i.e., developing data-driven models, the availability of building performance data, and stimulation of industrial practices. The review results indicate that data-driven methods were commonly applied to estimate energy consumptions, and explore energy trends, determinant features, and reference buildings. Identifying determinant features is one of the most successful applications. This study highlights the future research gaps for boosting data-driven building performance design.
date: 2021-09
date_type: published
publisher: Elsevier BV
official_url: https://doi.org/10.1016/j.jobe.2021.102706
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1867995
doi: 10.1016/j.jobe.2021.102706
lyricists_name: Wei, Shen
lyricists_id: SWEIX77
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Journal of Building Engineering
volume: 41
article_number: 102706
citation:        Tian, Z;    Zhang, X;    Wei, S;    Du, S;    Shi, X;      (2021)    A review of data-driven building performance analysis and design on big on-site building performance data.                   Journal of Building Engineering , 41     , Article 102706.  10.1016/j.jobe.2021.102706 <https://doi.org/10.1016/j.jobe.2021.102706>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10128881/1/1-s2.0-S2352710221005647-main.pdf