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鈥檚 head of research and innovation, Edwin Wealend, and trained using data obtained from UK government energy performance assessments.
Cosma said the research 鈥渋s an important first step towards the use of machine learning tools for energy prediction in the UK鈥� and it shows how data can 鈥渋mprove current processes in the construction industry鈥�.
In the UK, an EPC must be completed every time a building is sold, rented or constructed. It provides an indication of the energy efficiency of a building, contains information about the building鈥檚 typical energy costs, and recommends ways to make it more energy-efficient.
One of the key values returned is the asset rating 鈥� a number that gives a simple overall energy rating for a building and the number is banded (A+ to G) and colour-coded for ease of interpretation. However, producing this rating can be a slow process as the calculation requires the building emission rate (BER) 鈥� which can take hours to days to generate, depending on the complexity of the building.
In their latest paper, Cosma and the team show their AI system can generate a BER for non-domestic buildings in less than a second and with as few as 27 variables with little loss in accuracy 鈥� making it a much faster and efficient process. They used a 鈥榙ecision tree-based ensemble鈥� 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 鈥� such as shops, offices, factories, schools, restaurants, hospitals, and cultural institutions 鈥� 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.
Cosma said: 鈥淪tudies 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鈥檚 total CO2 别尘颈蝉蝉颈辞苍蝉.鈥�
Wealend added: 鈥淓ventually, 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.鈥�
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