A Machine Learning Approach to Improving Building Retrofit Plans in the UK
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Recent changes to the UK's Climate Change Act now require UK emissions to reach net zero by 2050, a 78% reductions in emissions compared to 1990 by 2035 and a a 63% reduction from 2019 levels. The UK's housing stock is one of the least energy-efficient in Europe: including indirect emissions, buildings are responsible for 23% of UK's total annual emissions. Emissions from homes need to fall by at least 24% from 1990 levels in order to hit net-zero plans. To meet these targets requires almost complete decarbonisation of the UK's building stock, facilitated by three main interventions: behavioral change, energy efficiency measures and fuel-switching. It is estimated that 70% of total 2010 building stock will still be in use in 2050 [5], which means that alongside building green new-builds, renovation of current inefficient stock is needed in order to hit emissions reduction targets. Delivering retrofit at national scales is challenging, particularly the pre-retrofit building surveyance, which historically has been a manual process. The risks of not decarbonising the housing stock include the UK Government missing the targets set out in law, financial risks to homeowners from insurance premiums increasing for non-retrofitted houses and health risks to inhabitants from buildings lacking appropriate thermal comfort.
Proposed Work:
1. To help with the practical planning of retrofit of existing buildings in the UK through a systematic assessment of the whole of the UK building stock, in a novel manner customised for retrofit planning. The goal is to create a ranked list and prioritisation map at varying geographic levels to be used in prioritising retrofit in the UK. This will include partnering with the Department for Business, Energy and Industrial Strategy (BEIS).
2. To quantify the impact high-resolution thermal imaging data has on improving retrofit planning and understanding thermal comfort.
3. To build an online tool for housing occupants to use to estimate the potential savings due to retrofitting their homes.
University of Cambridge | LEAD_ORG |
Jonathan Cullen | SUPER_PER |
Grace Beaney Colverd | STUDENT_PER |
Subjects by relevance
- Emissions
- Energy efficiency
- Buildings
- Climate changes
- Building stock
- Renovation building (activity)
- Greenhouse gases
- Housing stock
- Energy saving
- Decrease (active)
- Construction
- Climate Change Act
- Thermal comfort
Extracted key phrases
- UK building stock
- UK emission
- Improving Building Retrofit plan
- Machine Learning Approach
- UK Government
- Emission reduction target
- Retrofit building surveyance
- Total annual emission
- Housing stock
- Retrofit planning
- Indirect emission
- Current inefficient stock
- Recent change
- Climate Change Act
- Appropriate thermal comfort