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University of Cambridge’s AI model will help decarbonise city’s buildings

There’s been plenty of discussion about AI’s possible dangers in the UK recently, but meanwhile the technology’s potential is being used to identify houses which need decarbonising.

Decarbonisation - the removal or reduction of carbon dioxide output into the atmosphere - can be achieved by switching to low carbon energy sources or by retrofitting.

House heating
House heating

Identifying which properties should be retrofitted - usually this means insulating them - is now possible thanks to a new ‘deep learning’ model trained by researchers from Cambridge University’s Department of Architecture.

The first-of-its-kind AI model can help policymakers efficiently identify and prioritise houses for retrofitting and other decarbonising measures, starting with hard-to-decarbonise (HtD) buildings. These houses are responsible for a quarter of all direct housing emissions – a major obstacle to achieving net zero – but are rarely identified for improvement.

Houses can be ‘hard to decarbonise’ for various reasons including their age, structure, location, social-economic barriers and availability of data.

The researchers trained their AI model using data for their home city of Cambridge, in the United Kingdom. They fed in data from Energy Performance Certificates (EPCs) as well as data from street view images, aerial view images, land surface temperature and building stock. In total, their model identified 700 HtD houses and 635 non-HtD houses. All of the data used was open source.

Assessing heat leakage from properties. Picture: University of Cambridge
Assessing heat leakage from properties. Picture: University of Cambridge

The AI model has been created by Maoran Sun, an urban researcher and data scientist, and his PhD supervisor Dr Ronita Bardhan (Selwyn College), who leads Cambridge’s Sustainable Design Group.

Dr Bardhan said: “This is the first time that AI has been trained to identify hard-to-decarbonize buildings using open source data to achieve this.

“Policymakers need to know how many houses they have to decarbonize, but they often lack the resources to perform detail audits on every house. Our model can direct them to high priority houses, saving them precious time and resources.”

Maoran said: “ “We trained our model using the limited EPC data which was available. Now the model can predict for the city’s other houses without the need for any EPC data.

“This data is available freely and our model can even be used in countries where datasets are very patchy. The framework enables users to feed in multi-source datasets for identification of HtD houses.”

He added: “Our models will increasingly help residents and authorities to target retrofitting interventions to particular building features like walls, windows and other elements. Even very simple street view photographs can offer a wealth of information without putting anyone at risk.”

The researchers argue that “by making data more visible and accessible to the public, it will become much easier to build consensus around efforts to achieve net zero”.

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