Petrou, Giorgos;
(2023)
Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Adverse effects to health and wellbeing from increased exposure to heat at home has been repeatedly identified as a major climate change adaptation risk in the United Kingdom by the Climate Change Committee and others. Despite recent progress, policy gaps in the adaptation of the housing stock exist. The development of such policies can be guided by housing stock models, that enable the assessment of the impact of climate change adaptation and energy efficiency measures on building performance under different climate scenarios. To ensure well-informed decision-making, uncertainties in these models should be considered. Motivated by the lack of work on this topic, this thesis aims to quantify and reduce uncertainties of archetype-based housing stock models of summer indoor temperature through a Bayesian calibration framework. The framework includes the data-driven classification of dwellings into homogeneous groups, the characterisation of model input uncertainty in the form of probability distributions – which can be used as calibration priors – and their reduction through Bayesian inference. The framework’s implementation was demonstrated using the ‘UK Housing Stock Model’ (a bottom-up model based on EnergyPlus), the 2011 English Housing Survey and Energy Follow-Up Survey (EHS-EFUS), and the 2009 4M survey in Leicester. The model’s root-mean-square error reduced from 2.5 ⁰C (pre-calibration) to 0.6 ⁰C (post-calibration), while input and structural uncertainties were quantified. This work offers several novel contributions, including a modular framework that can be adapted for the improvement of other archetype-based housing stock models, an open-source method for identifying model input probability distributions, and an alternative formulation of Gaussian processes that substantially reduces the computational cost of Bayesian calibration. Learnings from this first calibration of its type can inform future academic research. Finally, the analysis of 2011 EHS-EFUS provides evidence to building designers and policymakers on the dwelling and household characteristics associated with high summer indoor temperatures.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | Indoor Overheating, Model Uncertainty, Bayesian Calibration, Domestic, Housing Stock Modelling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163978 |
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