EnergyEye
Expected start: October 2027Knowledge-informed performance-based digital twinning systems for community-scale energy-efficient residential building retrofits
Bringing together artificial intelligence, digital twins and building knowledge to support better decisions for home retrofits.
- Project type
- UCL EPSRC Doctoral Landscape Award
- Research theme
- AI, Digital Twins & Sustainable Buildings
- Academic host
- UCL | Bartlett School of Sustainable Construction | Centre for Global Infrastructure Resilience
- Supervisors
- Prof. Stergios-A Mitoulis
- Prof. Qiuchen Lu
Smarter evidence for residential retrofit
EnergyEye investigates how AI, digital twins and building knowledge can improve retrofit planning for existing homes. The research will connect building data, regulatory requirements and design evaluation to support transparent decisions that consider energy use, carbon, cost and occupant wellbeing.
From building data to informed decisions
Digital reconstruction
Creating geometric and semantic digital twins of existing homes from images, drawings and 3D point clouds.
Knowledge and compliance
Connecting building knowledge with machine-readable UK requirements to support automated compliance checking.
Generative retrofit design
Investigating knowledge-informed generative AI to produce and compare retrofit options against multiple performance criteria.
Integration and validation
Bringing the methods together in an EnergyEye research prototype and evaluating them using real housing datasets.
Academic and industry collaboration
University College London
The Bartlett School of Sustainable Construction
Centre for Global Infrastructure Resilience
Supervisors
Professor Qiuchen Lu
Professor Stergios-Aristoteles Mitoulis
Planarific
Supporting the research with labelled 3D datasets covering more than 15,000 homes, cloud resources, industrial expertise and a research placement in Cambridge.
Supporting lower-carbon, healthier homes
EnergyEye aims to deliver validated methods and a research prototype that help housing stakeholders assess retrofit options and support scalable improvements to residential energy performance.
Apply for the EnergyEye PhD
Explore the project and follow UCL’s application guidance.
Project ID: 2531cc1008