AutoEPC - Scalable, Accurate, Automated Building Fabric Assessment
Lead Participant:
DIGILAB SOLUTIONS LTD
Abstract
AutoEPC is a commercially scalable solution that uses self-learning algorithms to provide accurate fabric performance with no specialist equipment, installation or monitoring. According to the European Commission, household energy consumption accounts for roughly 20% of total CO2 emissions in UK and Europe, and heating losses are responsible for half of this figure. It is therefore essential to understand buildings' fabric performance in order to reduce their environmental footprint and incentivise projects that align with net zero goals. Existing EPC certificates are too inaccurate due to manual input and calculations, whilst expensive sensors setups combined with long term controlled testing aren't cost effective and therefore scalable across domestic and industrial markets.
This project address the technical challenges for large scale commercial adoption, focusing on automation of model setup, data efficient robust to noisy and limited data and exploring the opportunities good quantification of fabric performance in a home can have in realising carbon crediting or incentive opportunities of domestic and commercial buildings.
This project address the technical challenges for large scale commercial adoption, focusing on automation of model setup, data efficient robust to noisy and limited data and exploring the opportunities good quantification of fabric performance in a home can have in realising carbon crediting or incentive opportunities of domestic and commercial buildings.
Lead Participant | Project Cost | Grant Offer |
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DIGILAB SOLUTIONS LTD | £249,155 | £ 174,408 |
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Participant |
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GOOD HOMES ALLIANCE |
People |
ORCID iD |
Andrew Corbett (Project Manager) |