Technical Report 331, c4e-Preprint Series, Cambridge

Authors: Jiying Chen, Jiaru Bai, Jieyang Xu, Feroz Farazi, Sebastian Mosbach, Jethro Akroyd, and Markus Kraft*

Reference: Technical Report 331, c4e-Preprint Series, Cambridge, 2024

Associated Themes:
 

• Machine-understandable representation of energy and environmental data
• Implemented multi-criteria decision-making framework
• Formulated national building retrofit strategy and analysed policy disparities
• Household-level 'fabric first' vs 'system-led' retrofit recommendations
• Presented insights at county, local authority, and constituency levels

The upgrading of energy-inefficient buildings is a critical part of the energy transition. Holistic analyses that foster informed and equitable policy interventions require interoperable data. We apply a principled approach that leverages The World Avatar to create a virtual knowledge graph underpinned by machine-understandable data representations. This approach provides a common terminology to integrate heterogeneous data sources to support multi-scale analysis of building energy retrofit options. We consider a case study in the UK based on the holistic analysis of household-level energy performance data, public health statistics and socio-economic metrics across geographic hierarchies. The analysis identifies regions with critical retrofit necessities, revealing disparities between these imperatives and extant policy levers. Granular retrofit targets are proposed to optimise resource allocation to the most vulnerable areas. Bespoke retrofit strategies are developed for 14.4 million house- holds in the UK, providing actionable insights to support the targeted application of ‘fabric-first’ or ‘system-led’ retrofit pathways.

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