AI system predicts building energy rates ‘almost instantly’

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Computer scientists at Loughborough University have teamed up with multi-disciplinary engineering consultancy Cundall to create an AI system that can predict building emissions rates (BER) of non-domestic buildings.

Current methods to produce BERs can be laborious and take hours, but Dr Georgina Cosma and postgraduate student Kareem Ahmed, of Loughborough University’s School of Science, have designed and trained an AI model to predict BER values with 27 inputs almost instantly 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 data contained information such as building capacity, location, heating, cooling lighting and activity.

The team focused on calculating the rates for non-domestic buildings – such as shops, offices, factories, schools, restaurants, hospitals, and cultural institutions – as these are some of the most inefficient buildings in the UK in terms of energy use, so understanding how to improve their efficiency can be useful in design and renovation processes, the university said.

The paper is to be published on CIBSE’s website later this year.

Dr 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”.

She added: “Studies on the applications of machine learning on energy prediction of buildings exist, but these are limited, and even though they only make up 8% of all buildings, non-domestic buildings account for 20% of UK’s total CO2 emissions.”

The project was supported by Cundall’s head of research and innovation, Edwin Wealend, who added: “Eventually, we hope to build on the techniques developed in this project to predict real operational energy consumption.

“By predicting the energy consumption and emissions of non-domestic buildings quickly and accurately, we can focus our energy on the more important task – reducing energy consumption and reaching net zero.”

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