Dr Georgina Cosma and postgraduate student Kareem Ahmed of the School of Science, have designed and trained an AI model to predict building emissions rates values with 27 inputs with little loss in accuracy. The proposed AI model – which was created with the support of Cundall's head of research and innovation, Edwin Wealend, and trained using large-scale data obtained from UK government energy performance assessments – can generate a BER value almost instantly. Dr Cosma says the research "is an important first step towards the use of machine learning tools for energy prediction in the UK" and it shows how data can "improve current processes in the construction industry". In their latest paper, Dr Cosma and the team reveal their AI system can generate building emissions rates for non-domestic buildings in less than a second and with as few as 27 variables with little loss in accuracy. They used a'decision tree-based ensemble' machine algorithm and built and validated the system using 81,137 real data records that contain information for non-domestic buildings over the whole of England from 2010 to 2019.

The AI system can generate an almost instant prediction of building emissions rates (BER) for use in calculating the energy performance of non-domestic buildings. Current methods can take hours to days to produce BERs and are generated by manually inputting hundreds of variables. Dr Georgina Cosma and postgraduate student Kareem Ahmed, of the School of Science, have designed and trained an AI model to predict BER values with 27 inputs with little loss in accuracy. The model has been created with the support of Cundall's head of research and innovation, Edwin Wealend, and trained using data obtained from UK government energy performance assessments. Cosma said the research "is an important first step towards the use of machine learning tools for energy prediction in the UK" and it shows how data can "improve current processes in the construction industry".