The deep learning model was trained by researchers from Cambridge University’s Department of Architecture to identify ‘Hard-to-Decarbonise’ (HtD) houses, which are responsible for over a quarter of all direct housing emissions but are rarely identified or targeted for improvement.
HtD houses are so called for various reasons, including their age, structure, location, social-economic barriers and availability of data. “Policymakers have tended to focus mostly on generic buildings or specific hard-to-decarbonise technologies, but the study could help change this,” the researchers said.
Maoran Sun, an urban researcher and data scientist, and his PhD supervisor Dr Ronita Bardhan, who leads Cambridge’s Sustainable Design Group, said their AI model can classify HtD houses with 90% precision. They expect this to rise as they add more data, which is already underway.
Dr Bardhan said: “This is the first time that AI has been trained to identify hard-to-decarbonise buildings using open-source data to achieve this.
“Policymakers need to know how many houses they have to decarbonise, but they often lack the resources to perform detailed audits on every house. Our model can direct them to high priority houses, saving them precious time and resources.”
The model could also help authorities understand the geographical distribution of HtD houses, enabling them to target and deploy interventions efficiently.
The researchers trained their AI model using data from the city of Cambridge. 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.
Sun 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.”
Dr Bardhan added: “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.”
The researchers are now working on an even more advanced framework, which will bring additional data layers relating to factors including energy use, poverty levels and thermal images of building facades. They expect this to increase the model’s accuracy but also to provide even more detailed information.
The model is already capable of identifying specific parts of buildings, such as roofs and windows, which are losing most heat, and whether a building is old or modern.
The team is already training AI models based on other UK cities using thermal images of buildings, and are collaborating with a space organisation to benefit from higher resolution thermal images from new satellites.
Sun said: “Our models will increasingly help residents and authorities to target retrofitting interventions to particular building features like walls, windows and other elements.”
Until now, decarbonisation policy decisions have been based on evidence derived from limited datasets, Dr Bardhan said, but AI could change that. “We can now deal with far larger datasets. Moving forward with climate change, we need adaptation strategies based on evidence of the kind provided by our model. Even very simple street view photographs can offer a wealth of information.”
By making data more visible and accessible to the public, the researchers claimed it will become much easier to build consensus around efforts to achieve net zero.
“Empowering people with their own data makes it much easier for them to negotiate for support,” Dr Bardhan said. “There is a lot of talk about the need for specialised skills to achieve decarbonisation, but these are simple datasets and we can make this model very user friendly and accessible for the authorities and individual residents.”
The work was published in the journal Sustainable Cities and Society.
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