At a time when the UK is trying to reach net zero by 2050, the timely, accurate calculation of energy efficiency is a vital component of the design process for new and refurbished buildings.
Computer scientists at Loughborough University have teamed up with multi-disciplinary engineering consultancy Cundall to create an artificial intelligence system that can predict building emissions rates (BER) – an important value used to calculate building 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.
Better yet, the proposed AI model – which was created with the support of Cundall’s Head of Research and Innovation, Edwin Wealend, and trained using large-scale data obtained from UK government energy performance assessments – can generate a BER value almost instantly.
Dr Cosma says 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”.
So, what is a BER and why is it important?
To understand the importance of BERs, we must first discuss energy performance certificates (EPCs).
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’s typical energy costs, and recommends ways to make it more energy-efficient.
One of the most useful values returned is the asset rating – 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.