Tender Match
EU · in EnglishHORIZON-CL5-2026-11-D3-23

Data sharing to support the training and development of AI foundation models in the energy sector

Expected Outcome: Projects are expected to contribute to all the following expected outcomes: Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security. Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, throug

The official English text of the call comes from the European Commission's Funding & Tenders portal.

Deadline

1 December 2026 38 days left

Grant per project

10 million EURTotal budget: 30 million EUR · about 3 funded projects

Region

International (EU)

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Eligibility criteria

EU direct call: eligibility depends mainly on the consortium, the participants' countries and their types of organisation. We cannot check these from your company details, so we do not tell you whether you are eligible. The criteria are in the official call.

Official call

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What is this call about?

Az EU-s pályázat energetikai mesterséges intelligencia fejlesztésére irányul, amelynek keretében olyan szervezetek nyújthatnak be javaslatot, amelyek nagy energetikai adathalmazokkal rendelkeznek és képesek azokat biztonságosan megosztani. A támogatás célja olyan alapmodelleket létrehozni, amelyek az energiarendszer digitalizálását segítik előrejelzés, hálózatirányítás, megújuló energia integrálása és energiahatékonyság terén. A projektek során szükséges az adatmegosztási stratégiák kidolgozása, a modellek tesztelése valós körülmények között, valamint az érintett szereplők közötti együttműködés erősítése az energiaszektor átalakulásának felgyorsítása érdekében.

Official description

Expected outcome and scope

Expected Outcome: Projects are expected to contribute to all the following expected outcomes: Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security. Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, through improved grid observability, forecasting of supply and demand, advanced storage and renewables integration, demand side flexibility and energy efficiency. Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange.

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