Home » Scientists develop AI model that predicts building emissions rates ‘quickly and accurately’

Scientists develop AI model that predicts building emissions rates ‘quickly and accurately’

by Liam Turner
Greenery surrounding tall buildings

An AI model that can predict building emissions rates (BER) of non-domestic buildings with a high degree of accuracy and speed has been developed in the UK.

Dr Georgina Cosma and postgraduate student Kareem Ahmed, of Loughborough University’s School of Science, teamed up with multi-disciplinary engineering consultancy Cundall to create an AI system that can predict BERs values with 27 inputs almost instantly with little loss in accuracy.

The team 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, and factories – as these are some of the most inefficient buildings in the UK in terms of energy use.

Understanding how to improve their efficiency can be useful in design and renovation processes, the university said.

The model was developed in an effort to address the laborious and time-consuming nature of current BER-production methods.

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

Reaching net-zero

Dr Cosma said the research was “an important first step” toward the use of machine learning tools for energy prediction in the UK and improving current processes in the construction industry.

She said: “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.”

Edwin Wealend, Cundall’s head of Research and Innovation – which supported the project – said: “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.”

Image credit: mokokomo/Shutterstock

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