Energy
4 labs · 4 models and products Theme: Companies · Generated from this page's data. Click a name to open its panel on the Labs page. Records resting on one source are tagged "one source".AI for energy applies machine learning to running power stations, oil, gas and chemical plants and electricity grids, and, in research, to fusion power. The government's AI for Science Strategy says AI will be "instrumental" to the UK's pursuit of fusion energy, and ministers set up an AI Energy Council with industry in 2025. The one lab on this tile, Applied Computing, is a London company building AI foundation models for oil, gas and petrochemical operations, sold through its Orbital platform (per the site).
Context
- The AI for Science Strategy says AI will be central to the UK's pursuit of commercial fusion energy. 20 November 2025 gov.uk/government/publications/ai-for-science-st
AI will be instrumental in the UK’s pursuit of commercialised fusion energy
- The UK Compute Roadmap lists nuclear fusion and materials science among the research the national compute expansion will support. 17 July 2025 gov.uk/government/publications/uk-compute-roadma
frontier physics such as nuclear fusion, materials science, medical research and quantum technology.
- The UK's AI Energy Council, co-chaired by the Technology and Energy Secretaries, held its first meeting. 8 April 2025 gov.uk/government/news/technology-and-energy-sec
UK's new AI Energy Council held its first meeting in Westminster today
- The council's remit covers both power for AI and AI's use in running the energy system. 8 April 2025 gov.uk/government/news/technology-and-energy-sec
can serve the energy system too
- Applied Computing, headquartered in London, was reported to have raised $20 million in a round led by engineering firm KBR (trade; reported). 16 July 2026 tech.eu/2026/07/16/applied-computing-lands-20m-t
a British artificial intelligence company developing foundation models for energy operations.
Labs Round 8 (31 Oct 2026): introduction and facts as the researchers wrote them; "(per the site)" marks a sentence resting on this site's own description of that lab.
| Record | What it is | Place | Links | Confidence |
|---|---|---|---|---|
| Applied ComputingCompanies · also on Start-ups | London company building AI foundation models for oil, gas and petrochemical operations, sold through a platform called Orbital.Profile
Labs Round 7 deep profile (30 Oct 2026), as the researchers wrote it; confidence: single_source (three trade sources; appliedcomputing.com blocked). Sources Models and products (1)
Key facts and news (6)
Labs Round 8 (31 Oct 2026), from the researchers' rows; one-source items are marked. | London (EC2A 4NE) | Companies House · tech.eu/2026/07/16/applied-computing-lands-20m-t · find-and-update.company-information.service.gov. · find-and-update.company-information.service.gov. · find-and-update.company-information.service.gov. | one source |
| digiLabCompanies · also on Start-ups | Exeter company that builds AI and machine-learning tools, employing Exeter graduates.Models and products (1)
Key facts and news (1)
Labs Round 8 (31 Oct 2026), from the researchers' rows; one-source items are marked. | Kings Wharf, Exeter (EX2 4AN ) | Companies House · news.exeter.ac.uk/?p=22533 · find-and-update.company-information.service.gov. · channellife.co.uk/story/ai-speeds-uk-fusion-toka · find-and-update.company-information.service.gov. | one source |
| GB National Solar Forecast (Open Climate Fix AI solar forecast)Chips, models and products | Open Climate Fix's AI forecast of solar generation in Great Britain, combining satellite images, weather predictions and live data; used daily by NESO. in useKey facts Forecasts minutes to hours ahead; began as a NESO innovation pilot, now used daily in NESO's control room (OCF, as read 31 Oct 2026; no date given); open-source sibling Quartz Solar forecast on GitHub (openclimatefix/open-source-quartz-solar-forecast)Made by Open Climate Fix · 2026-10-31 (as read) | County Hall, London (SE1 7PB) (via Open Climate Fix) | openclimatefix.org/work/solar-forecasting · openclimatefix.org/our-model | one source |
| Open Climate FixCompanies · also on Start-ups | A not-for-profit that builds open machine-learning forecasts of solar power, used by Britain's grid operator to balance the system.Models and products (1)
Key facts and news (3)
Labs Round 8 (31 Oct 2026), from the researchers' rows; one-source items are marked. | County Hall, London (SE1 7PB) | Companies House · openclimatefix.org/insights/neso-adopts-ai-solar · pv-magazine.com/2025/11/07/ai-powered-solar-fore · find-and-update.company-information.service.gov. · digicatapult.org.uk/about/press-releases/post/la | verified |
| Orbital (Applied Computing energy foundation model)Chips, models and products | Applied Computing's AI for oil refineries and petrochemical plants, combining plant sensor data, physics and a chemical-engineering language model. live (product)Key facts Three parts: time-series model on raw plant data, physics-based model that extracts equations from manuals and literature, and a language model trained on chemical-engineering knowledge; company figures (28 May 2025): outperforms benchmarked software peers by about 90% on key metrics, average 75% cost cut vs cloud AI, payback under 10 weeks, uses 100% of facility data vs 8%; case study (28 Aug 2024) on a simulation of Reliance's Jamnagar refinery found $119K annual hydrotreater savings; INSITE 3.0 "powered by Orbital" with KBR shown 24 Mar 2026; runs natively on AWS; not related to Orbital Industries (BIZ-LAB-024) or Orbital (BIZ-SU-LEG-011)Made by Applied Computing · — (flagship product named in release of 28 May 2025; first public date not stated) | London | appliedcomputing.com/press-release-28052025 · appliedcomputing.com/more/partnerships | lab's own pages |
| TokaMind (fusion plasma foundation model)Chips, models and products | Open-source foundation model for fusion plasma, trained on data from UKAEA's MAST tokamak by IBM Research, UKAEA and the Hartree Centre. released (open source)Key facts Base 9,324,382 and tiny 5,293,854 parameters; pre-trained on public MAST data; TokaMark benchmark of 14 tasks, 11,573 shots, 39 signals (arXiv v1, 16 Feb 2026); did better than a traditional machine-learning model on nearly all 14 tasks; MAST ran 1999–2013 (IBM Research blog, 23 Apr 2026); code at github.com/UKAEA-IBM-STFC-Fusion-FMs/tokamindMade by Hartree Centre · 2026-02-16 | Abingdon | arxiv.org/html/2602.15084v1 · research.ibm.com/blog/fusion-plasma-ai-model | lab's own pages |
| UK Atomic Energy Authority (UKAEA) fusion AI and computing programmeNetworks, bodies, events and media | The UK Atomic Energy Authority trains AI models on fusion experiment data, including TokaMind, a plasma foundation model built with IBM and Hartree.Key facts TokaMind posted 16 Feb 2026 (base 9,324,382 parameters; tiny 5,293,854), trained on MAST tokamak data (arXiv); 14 Sep 2026: declaration of intent with US PPPL to link SUNRISE (backed by £45m UK government money) and STELLAR-AI and train the same AI models on MAST Upgrade and NSTX-U data, long-term aim a shared foundation model for spherical tokamaks (WNN, 15 Sep 2026); 14 Jan 2026: digiLab models let UKAEA explore reactor designs around 100,000 times faster (company figure; channellife)Models and products (1)
| Abingdon (arXiv author affiliation) | arxiv.org/html/2602.15084v1 · world-nuclear-news.org/articles/uk-usa-enhance-c | verified |
| Uncertainty Engine (digiLab), as used for UKAEA plasma-turbulence modelsChips, models and products | digiLab's machine-learning tool, used with UKAEA to build plasma-turbulence models with quantified uncertainty for spherical tokamak designs. in useKey facts Models let UKAEA explore reactor designs around 100,000 times faster for relevant workloads; redundant simulations cut by a factor of four; also used Bayesian optimisation for sensor placement (company figures; channellife, 14 Jan 2026)Made by digiLab · 2026-01-14 | Kings Wharf, Exeter (EX2 4AN ) (via digiLab) | channellife.co.uk/story/ai-speeds-uk-fusion-toka | one source |