AI system developed to predict building energy rates

Building emissions rates (BER) are an important component of calculating energy performance and efficiency in buildings, and are required for the completion of a building’s energy performance certificate (EPC).

Current methods of producing BERs are generated by manually inputting hundreds of variables, which can take hours to days to generate depending on a building’s complexity.

A research team led by Dr Georgina Cosma and postgraduate student Kareem Ahmed, of Loughborough’s School of Science, now claims to have designed and trained an AI model that can predict BER values of non-domestic buildings in less than a second, with as few as 27 variables with little loss in accuracy.

Dr Cosma described the research as ‘an important first step towards the use of machine learning tools for energy prediction in the UK’, showing how data can ‘improve current processes in the construction industry’.

Created with support from Cundall’s head of research and innovation Edwin Wealend, the AI model was reportedly trained using large-scale data obtained from UK government energy performance assessments.