eprintid: 10199852 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/19/98/52 datestamp: 2024-11-11 11:45:26 lastmod: 2024-11-11 11:45:26 status_changed: 2024-11-11 11:45:26 type: article metadata_visibility: show sword_depositor: 699 creators_name: Petrou, Giorgos creators_name: Mavrogianni, Anna creators_name: Symonds, Phil creators_name: Chalabi, Zaid creators_name: Lomas, Kevin creators_name: Mylona, Anastasia creators_name: Davies, Michael title: Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature ispublished: inpress divisions: UCL divisions: B04 divisions: C04 divisions: F34 keywords: Bayesian calibration; archetype-based modelling; housing stock model; indoor temperature; uncertainty quantification; performance gap note: © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. abstract: Archetype-based housing stock models of summer indoor temperature can support the development of policies to manage the climate change-driven increase in cooling demand and heat-related health impacts. Calibration can reduce the performance gap of such models, however, work on this topic is limited. Motivated by the growing importance of this underexplored research area, this paper introduces a framework for the Bayesian calibration of archetype-based housing stock models of summer indoor temperature. The framework includes data-driven procedures to classify dwellings into homogeneous groups and specify prior probability distributions. To demonstrate its application, an established bottom-up model based on EnergyPlus was calibrated using data collected from 193 dwellings monitored during the 2009 4M survey in Leicester, England. Post-calibration, the root-mean-square error reduced from 2.5°C to 0.6°C and remaining uncertainties were quantified. The application of this modular framework may be extended to models of energy use and other indoor environmental parameters. date: 2024-11-04 date_type: published publisher: Informa UK Limited official_url: https://doi.org/10.1080/19401493.2024.2421330 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2334239 doi: 10.1080/19401493.2024.2421330 lyricists_name: Petrou, Giorgos lyricists_name: Davies, Michael lyricists_name: Mavrogianni, Anna lyricists_name: Symonds, Philip lyricists_id: GPETR43 lyricists_id: MDAVI86 lyricists_id: AMAVR49 lyricists_id: PSYMO82 actors_name: Petrou, Giorgos actors_id: GPETR43 actors_role: owner full_text_status: public publication: Journal of Building Performance Simulation issn: 1940-1493 citation: Petrou, Giorgos; Mavrogianni, Anna; Symonds, Phil; Chalabi, Zaid; Lomas, Kevin; Mylona, Anastasia; Davies, Michael; (2024) Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature. Journal of Building Performance Simulation 10.1080/19401493.2024.2421330 <https://doi.org/10.1080/19401493.2024.2421330>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10199852/1/Development%20of%20a%20Bayesian%20calibration%20framework%20for%20archetype-based%20housing%20stock%20models%20of%20summer%20indoor%20temperature.pdf