GrantWise
HORIZON EUROPE TOPIC · Cluster 5 · Climate, Energy and Mobility

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

HORIZON-CL5-2026-11-D3-23 — the expected outcomes as checkable statements, what evaluators look for in this topic, and the conditions that shape the proposal.

Get the free Part B working draft for this topic ↓

OpenIADeadline 1 December 2026around EUR 10 million per project

GrantWise › Horizon Europe topics › HORIZON-CL5-2026-11-D3-23

TopicHORIZON-CL5-2026-11-D3-23
StatusOpen
CallHORIZON-CL5-2026-11 — ENERGY
DestinationEnsuring more efficient, sustainable, secure, and competitive renewable and decarbonised energy supply (2026-27)
Type of actionInnovation Actions
Opening4 August 2026
Deadline1 December 2026
Budgetaround EUR 10 million per project · 3 projects expected · EUR 30 million in total
TRL at the endTRL 7–8
Part B page limit40 pages (standard application form; check your call)
TemplateWork Programme 2026–2027 application form

Expected outcomes

Projects should contribute to all of the expected outcomes. The topic says: “Projects are expected to contribute to all the following expected outcomes:”

  1. EO1 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.
  2. EO2 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.
  3. EO3 Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange.
  4. EO4 Advanced methodologies for AI model development ensuring that the results are FAIR (Findable, Accessible, Interoperable, Reusable) beyond the project ending, open source and accommodating for technology evolution.

Expected impacts named in the destination

The wider, long-term effects the destination seeks. Your outcomes feed them; they are not project deliverables.

  1. EI1 Energy producers have access to efficient and competitive European renewable energy and renewable fuel technologies with a solid knowledge base and are able to deploy them to enhance the EU’s energy security and reach its climate neutrality objectives, in a sustainable way in environmental (e.g., biodiversity, multiple uses of land and water, natural resources, pollution) and socioeconomic terms, and in line with the Sustainable Development Goals.
  2. EI2 Technology providers have access to European, competitive, resilient, reliable, sustainable, and affordable value chains of renewable energy and renewable fuel technologies including emerging ones, and with strong export potential to supply both the EU internal and global markets. They benefit also from circular renewable energy technologies that are safe and sustainable by design with reduced and diversified external dependence on critical raw materials.
  3. EI3 Economic sectors benefit from better integration of renewable energy and renewable fuel-based solutions that are, among others, competitive, cost-effective, efficient, flexible, reliable, and sustainable. Such integration is facilitated through digitalisation and integration of artificial intelligence of renewable energy technologies that provide network stability and reliability.
  4. EI4 European industries benefit from a reinforced export potential of renewable energy and renewable fuel technologies, also through international partnerships, and become more competitive in innovative renewable energy technologies in Europe and globally.
  5. EI5 European researchers benefit from a stronger community and from a reinforced scientific basis on renewable energy and renewable fuel technologies including emerging ones, also through international collaborations.
  6. EI6 European citizens have access to an energy market that is fair and equitable, more resilient, uses all different types of local renewable energy resources, and is less dependent on fossil fuels imports. Citizens experience less fuel and energy poverty, and also benefit from new employment and upskilling opportunities. Local communities benefit from a more decentralized, affordable, and secure energy system and from multiple uses of land and water.
  7. EI7 R&I actions will support the just digital and green transformation of the energy system through advanced solutions for accelerating the energy systems integration and decarbonisation. The developed clean, sustainable solutions will contribute to making the energy system work better for actors and supply more reliable, resilient and secure energy – even under increasingly more frequent extreme climate events.
  8. EI8 The solutions developed will contribute to increase flexibility and grid hosting capacity for renewables through optimizing cross sector integration and grid scale storage as well as cover off-grid situations. They will improve the preparedness of the electricity system to support the EU's binding target for 2030 of minimum of 42.5% renewables in the gross final energy consumption (with the aspiration to reach 45%), and full decarbonisation by 2050. They will enable further electrification of demand and will enhance the competitiveness of the European value chain, reduce pressure on resources (also by making technologies ‘circular by design’) and decrease dependencies. Such solutions would…
  9. EI9 The solutions will improve consumer awareness and engagement in the energy transition, via innovative offers and services (e.g. demand response, energy communities) and will target different types of consumers, including “hard to reach” population groups (such as energy poor or low-income households). This will result in increased trust in, and uptake of the new products and services entering the energy system.
  10. EI10 Accelerated deployment of carbon capture, use and storage (CCUS) as a CO2 emission mitigation option in electricity generation and/or in industry applications, as well as carbon dioxide removal for negative emissions.

What evaluators will look for in this topic

Expected outcomes

  • Projects should contribute to all of the expected outcomes.
  • For each outcome (EO1 to EO4), name who will use or adopt your results (a specific group, not “industry” or “society”), how they get them, and the indicator that shows the change, with baseline, target and date.
  • Use the topic's own words for each outcome, so the evaluator can tick it off.

What the scope asks for

Evaluators check the proposal against these sentences of the scope. Answer each one visibly, in the call's own words.

  • “Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus areas of the energy sector.”
  • “Building on the results of previously funded projects on the Common European energy data space as well as the ongoing work within the Data 4 Energy expert group, projects are expected to demonstrate innovative methods and data governance strategies for sharing data among various energy actors.”
  • “Projects are expected to demonstrate that they have access to large relevant datasets at the time of submitting the proposal.”
  • “Projects are expected to develop innovative strategies for sharing data in an effective, secure and transparent way to support the development of AI foundation models.”
  • “The data governance strategies that will be developed in the project should be designed in a way to be scalable and operational even after the end of the project.”
  • “Projects are expected to develop foundation models tailored for the energy sector, addressing at least one -or more- of the following use cases: planning and operation of electricity grids (including static power flow modelling and dynamic EMT modelling), forecasting, congestion management, anomaly detection, fault diagnosis, predictive maintenance, flexibility management, demand-side energy efficiency, smart and bidirectional charging of EVs…”
  • “Projects are expected to perform in-depth testing and validation of developed foundation models in lab demonstrators and real-life pilots, building on the results and using the facilities of previously funded projects on “AI testing and experimentation facilities (TEFs)”.”
  • “Projects are expected to bring together a wide group of stakeholders including data owners (for example energy utilities, grid operators, asset owners), application developers (for example startups, SMEs, hyperscalers) and model deployers (for example industry, grid operators, equipment manufacturers) providing a space of cooperation and collaborative data exchange.”
  • “AI models are expected to respect ethical, safety and security principles; furthermore, transparency, explainability, and accountability should be embedded by design in the model development.”
  • “Open-source development practices should be pursued wherever feasible.”
  • “Efforts should be made to avoid biases, ensuring that the data produced is representative of diverse populations.”
  • “Selected projects are expected to contribute to the BRIDGE initiative and actively participate in its activities.”

Conditions that shape the proposal

  • Part B, sections 1–3: 40 pages (standard application form). Text beyond the limit is not read; tables and figures count.
  • TRL: the topic expects TRL 7–8 by the end of the project. State where you start and the evidence for it.
  • Innovation action: evaluators expect a plausible path to commercialisation and named first users or customers.

Impact pathway and traps

  • The scope expects pilots or demonstration. Name the sites, the partners who run them and what is measured there.
  • Open science or data practices are named. Make the data management plan concrete: which data, which repository, when.
  • Policy or regulation is in scope. Name the policy owners you will reach and what you will give them.
  • Keep outputs, outcomes and impacts apart: what you deliver, what changes when others use it, and the long-term effect.
  • For each destination impact you claim, show which of your outcomes feeds it. Destination impacts are long-term effects, not project deliverables.
  • Name the barriers outside the project (regulation, markets, user behaviour) that could block uptake, and what you will do if they do.
  • Section 2.3 (the summary canvas) is optional on this call; the old split into scientific, economic and societal impact is no longer required.
The scope, as published

The quality of AI models depends strongly on the amount, quality, and representativeness of the data used for their training. Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus areas of the energy sector. Projects are expected to: Building on the results of previously funded projects on the Common European energy data space as well as the ongoing work within the Data 4 Energy expert group, projects are expected to demonstrate innovative methods and data governance strategies for sharing data among various energy actors. Projects are expected to demonstrate that they have access to large relevant datasets at the time of submitting the proposal. Projects are expected to develop innovative strategies for sharing data in an effective, secure and transparent way to support the development of AI foundation models. An indicative non exhaustive list of strategies that can be explored is the use of data space connectors or federated training of models to allow the integration of confidential data; the creation of synthetic data as an intermediate step to fill data gaps or improve the quality of the data could also be explored. Projects could also explore other innovative data sharing strategies, such as an ‘AI gym’. The data governance strategies that will be developed in the project should be designed in a way to be scalable and operational even after the end of the project. Projects are expected to develop foundation models tailored for the energy sector, addressing at least one -or more- of the following use cases: planning and operation of electricity grids (including static power flow modelling and dynamic EMT modelling), forecasting, congestion management, anomaly detection, fault diagnosis, predictive maintenance, flexibility management, demand-side energy efficiency, smart and bidirectional charging of EVs or other use cases that contribute to the objective of digitalisation of the energy system. Projects are expected to perform in-depth testing and validation of developed foundation models in lab demonstrators and real-life pilots, building on the results and using the facilities of previously funded projects on “AI testing and experimentation facilities (TEFs)”. Regulatory sandboxes could also be considered for real-life pilot implementations. Projects are expected to bring together a wide group of stakeholders including data owners (for example energy utilities, grid operators, asset owners), application developers (for example startups, SMEs, hyperscalers) and model deployers (for example industry, grid operators, equipment manufacturers) providing a space of cooperation and collaborative data exchange. To scale up the training and development of the models, projects can benefit from the computing capacity of the AI factories that were recently announced by the European Commission, in particular the three AI factories that aim to develop AI applications for the energy sector. AI models are expected to respect ethical, safety and security principles; furthermore, transparency, explainability, and accountability should be embedded by design in the model development. Open-source development practices should be pursued wherever feasible. Efforts should be made to avoid biases, ensuring that the data produced is representative of diverse populations. Selected projects are expected to contribute to the BRIDGE initiative and actively participate in its activities.

Technology readiness, as published

Activities are expected to achieve TRL 7-8 by the end of the project – see General Annex B. Activities may start at any TRL. Establish the grounds for a common European energy data space AI Testing and Experimentation Facility (TEF) for the energy sector – bringing technology to the market AI Factories https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/

Other topics in HORIZON-CL5-2026-11

Source and attribution

Topic text from the official topic page on the EU Funding & Tenders Portal, retrieved 19 October 2026. © European Union, reused under Commission Decision 2011/833/EU (CC BY 4.0). Changes: the expected outcomes and impacts are split into numbered statements, long passages are shortened, and the evaluator checklist is GrantWise's own commentary. Your call and the Work Programme always win; check them before you submit. GrantWise is independent and not affiliated with, endorsed by, or acting on behalf of the European Commission.