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).
According to the project, accurate calculations of energy efficiency is a vital component of the design process for new and refurbished 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 therefore designed and trained an AI model to predict BER values with 27 inputs with little loss in accuracy.
Cosma, explained: “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 AI model, created with the support of Cundall’s head of research and innovation, Edwin Wealend, was trained using large-scale data obtained from UK government energy performance assessments to generate a BER value almost instantly.
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”.
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 across England from 2010 to 2019. The data contained information such as building capacity, location, heating, cooling lighting, and activity.
Focusing 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, the team were able to understand how to improve their efficiency. This knowledge can then be useful in design and renovation processes.
Wealend, 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.”
The projects findings were published in a paper titled Predicting the Building Emission Rate of Non-Domestic Buildings Using Machine Learning.
