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