Conference Paper


Spatially-based urban energy modelling approach for enabling energy retrofits in Oxfordshire

Abstract

The UK government has committed to achieving net zero carbon emissions by 2050. This will require a transformation of the housing sector as it has lagged previous emissions targets. Although millions of existing homes across the UK need energy improvements, the process of identifying suitable and eligible homes is presently a time-consuming task and energy suppliers are struggling to meet their targets. To address this challenge, this paper describes the application of a data-driven geographical information system-based approach to spatially identify suitable dwellings quickly and accurately by mapping and modelling baseline energy use and potential for energy retrofit measures, singularly and in combination. Drawing on publicly available datasets on housing and energy, combined with local datasets, a neighbourhood with high fuel poverty in Bicester (Oxfordshire, UK) was selected. The DECoRuM model was then used to estimate current energy use and potential for energy reduction on a house-by-house level. The improvement measures were aggregated to encourage bulk installations and drive down installation costs. House-level energy assessment in the selected area using DECoRuM shows that a package-based approach comprising building fabric and heating system upgrade and solar PVs is effective at significantly reducing energy consumption and energy bills, as well as fuel poverty. This spatially based urban energy modelling approach brings together energy calculations and spatial mapping to address the barriers to mass retrofit programmes. The data collected can also be used to build brokering services amongst those who need energy improvements (households) with those can provide retrofit measures (installers) and those can sponsor energy measures (energy suppliers).

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Authors

Gupta, Rajat
Gregg, Matt

Oxford Brookes departments

School of Architecture

Dates

Year of publication: 2020
Date of RADAR deposit: 2020-12-03


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


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