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