Summary
Partner Search for Horizon Europe Call HORIZON CL5-2026-11-D3-23 on AI-Driven Data Sharing for the Energy Sector. The European strategic consultancy Tinexta Innovation Hub is preparing a proposal on this topic, whose main objective is to demonstrate, on a pan-European scale, a federated, secure, and interoperable energy data ecosystem for developing, training, testing, and validating foundational AI models applied to the energy sector. Fraunhofer is one of the partners in the consortium.
The consultancy is requesting a financial contribution of €5,000 per partner, plus a success fee of 2.5% of the grant awarded to the partner if the project is funded.
Description
Central Idea of the Project:
The proposal seeks to create a European “Energy AI Gym” infrastructure, that is, an environment for training, simulating, validating, and testing foundational AI models for energy.
The project would combine:
• Federated energy data spaces.
• AI testing infrastructure.
• Multimodal foundational models for energy.
• Real-world pilot projects with energy system stakeholders.
• Validation on datasets from TSOs, DSOs, renewables, EV charging, industry, storage, and energy markets.
Problem to be solved:
The document identifies several key barriers to applying advanced AI in the energy sector:
• Lack of large energy datasets for AI training.
• Low interoperability between operators, utilities, and market players.
• Limited access to real operational data.
• Privacy and cybersecurity risks.
• Dependence on non-European AI ecosystems.
• Legacy infrastructures with incompatible data formats. • Difficulty in deploying real-time AI on complex energy networks.
Main Innovation:
The innovation lies in creating the first scalable European framework for the collaborative training and deployment of foundational AI models for energy, combining:
• Federated energy data spaces.
• Federated learning.
• Sectoral AI foundation models.
• Validation in real-world environments.
• Common benchmarking and stress testing infrastructure.
• Data governance aligned with FAIR, the AI Act, the Data Act, and the Data Governance Act.
Expected Results:
The key deliverables would be:
• Operational infrastructure for experimentation, validation, and secure data access.
• Validated foundational AI models on extensive energy datasets.
• Governance, data sharing, and interoperability protocols.
• AI development methodologies that guarantee FAIR, reusable, and, where possible, open-source results.
• Recommendations for the future Common European Energy Data Space.
Target Partners:
• Utilities, TSOs, and DSOs: Contribution of real-world pilot projects, operational datasets, validation environments, network observability data, and flexibility case studies.
• HPC and AI training providers: Leadership in training foundational models specifically for the energy sector.
• Data space and interoperability stakeholders: Design of data governance, interoperability, federated learning, and secure exchange mechanisms.
• Legal/regulatory experts: Compliance with the AI Act, Data Act, Data Governance Act, ethics, FAIR, and operational governance.
• Research centers and SSH: Model optimization, socioeconomic and environmental assessment.
Should you have any questions, feel free to contact Mar Coromina mcoromina@secartys.org | 623 35 59 80, and we'll do our best to help you.
