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| Reference Number | UKRI2708 | |
| Title | AI-EDOL: AI Generated Synthetic Smart Meter Data | |
| Status | Started | |
| Energy Categories | Other Cross-Cutting Technologies or Research (Energy system analysis) 100%; | |
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 100% | |
| UKERC Cross Cutting Characterisation | Other 100% | |
| Principal Investigator |
Tadj Oreszczyn University College London |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 October 2025 | |
| End Date | 01 April 2026 | |
| Duration | 6 months | |
| Total Grant Value | £229,936 | |
| Industrial Sectors | Unknown | |
| Region | London | |
| Programme | Artificial Intelligence and Robotics | |
| Investigators | Principal Investigator | Tadj Oreszczyn , University College London |
| Other Investigator | Eoghan McKenna , University College London |
|
| Web Site | ||
| Objectives | ||
| Abstract | The Challenge The UK's journey to net zero depends on understanding how people use energy in their homes, but accessing the data needed for this research is restricted. Smart meter data from households provides crucial insights for developing better energy policies, reducing fuel poverty, and improving building performance. However, privacy regulations and data protection rules create major barriers to accessing this information, slowing down vital research that could accelerate our transition to clean energy. Our Solution We will solve this data access problem using cutting-edge artificial intelligence. Our project will train advanced AI models called Generative Pretrained Transformers (GPTs) on the UK’s world-leading SERL Observatory dataset - a unique collection of smart meter data from 13,000 representative GB households, combined with detailed information about their buildings, occupants, and energy use patterns collected over five years. The AI models will learn the complex patterns in real energy data and generate completely synthetic datasets that look like real household energy data but contain no actual personal information. This synthetic data will have all the statistical properties researchers need while being completely privacy-preserving. This project directly advances EPSRC AI for Science objectives: developing AI capabilities across research fields to accelerate scientific discovery; increasing access to well-governed, high-quality datasets for AI; building interdisciplinary collaborations between AI and energy researchers; and embedding AI as a research tool in a fair and inclusive way. What We Will Deliver Over six months, we will create "Synthetic-SERL", the first dual-fuel synthetic smart meter dataset with long temporal sequences. This will include half-hourly gas and electricity data for 13,000 virtual households across full calendar years, each with contextual information about building and occupant characteristics and weather. We will rigorously test this synthetic data to ensure it provides genuine research utility while passing strict privacy audits. The entire dataset, along with the training code and tools to integrate the data into workflows, will be published under open licences, making it freely available to researchers worldwide. Impact and Applications This research will deliver targeted benefits across three key sectors. Academia will benefit from removal of barriers to accessing high-quality data, enabling and accelerating R&D that was previously restricted by privacy regulations. Industry will gain access to data for testing business cases and grid planning that rely on high-resolution energy data, supporting investment decisions in clean energy technologies. Government will have data to evaluate distributional impacts of net zero policies like time-of-use tariffs, ensuring a fair transition for all households. By democratising access to high-quality energy data, we will unlock research that was previously restricted, accelerate innovation in the energy sector, and create new partnerships between AI researchers and energy experts. This project establishes the foundation for future research that will push the boundaries of AI for energy decarbonisation. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 07/01/26 | |
