The early-stage decision-making of modular facade retrofitting with renewable energy technologies - A case-based reasoning approach

Li, Yangluxi 2025. The early-stage decision-making of modular facade retrofitting with renewable energy technologies - A case-based reasoning approach. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Energy conservation and emission reduction are pressing global priorities, with buildings being a major energy consumer. Retrofitting existing structures offers significant potential to reduce energy use and carbon emissions. The Climate Change Act 2008 set the 2050 Net-Zero target, which requires the UK government to reduce the greenhouse emissions by 100% relative to 1990 levels for 2050. To further distribute this aim, the targets in retrofit are raised for at least a 32% share of renewable energy and at least a 32.5% improvement in energy efficiency. Approximately 27 million existing residential buildings need to be retrofitted in the UK. However, meeting national carbon goals within tight timelines is difficult due to the massive scale of housing. Modular retrofitting, which involves upgrading existing structures with prefabricated, modular components, holds great promise for improving building functionality, energy efficiency, and aesthetics without the need for complete demolition. This method is generally faster, less disruptive, and more cost-effective than traditional renovation methods. Post-war housing in the UK, often standardized and mass-produced, is particularly suitable for modular retrofits, integrating renewable technologies and improving energy efficiency. However, modular retrofitting is still underdeveloped, with most studies focusing on individual cases rather than scalable, broad-based solutions. Key barriers include housing diversity hindering universal strategies, insufficient systematic research linking modular designs, and a lack of stakeholder awareness. This research aims in developing a Case Based Reasoning (CBR) decision-making approach, encourage practitioners to consider potential applicable modular retrofit approaches rapidly, by providing the matched similar solutions on the renewable technologies in the early design stage. There are 2 research questions related to this aim: 1. What kind of mechanism can enable the CBR decision-making approach to achieve rapid selection of renewable energy technologies? 2. How to translate the knowledge of modular retrofit with renewable energy technologies into an integrated guide? Methodology: This research addresses these challenges by proposing a Design Research Methodology (DRM) to systematize retrofit design processes. By establishing structured research frameworks and methodological selection criteria, which could facilitate data-driven decision-making. A comprehensive repository of retrofit solutions permits comparative analysis and solution integration, while early-stage selection of renewable technologies streamlines retrofit workflows. This research pioneers a Case-Based Reasoning (CBR) decision-support framework, empowering practitioners to rapidly identify applicable modular retrofit strategies. Through pattern recognition in historical retrofit data, the CBR system provides context-specific renewable technology recommendations during critical early design phases. A CBR decision-support prototype for rapid retrofit strategy matching is constructed. The scalability of the research lies in its ability to transition from fragmented case studies to replicable, system-level interventions. By addressing the gaps in current approaches, the research establishes theoretical foundations that allow for more widespread and efficient implementation of sustainable building solutions. This scalable framework can be adapted across various contexts, making it possible to replicate successful interventions in diverse settings, ultimately driving broader adoption of sustainable technologies in the building sector.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Architecture
Date of First Compliant Deposit:	10 October 2025
Last Modified:	10 Oct 2025 15:09
URI:	https://orca.cardiff.ac.uk/id/eprint/181591

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