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Artificial Intelligence Experimentation Facility For the Energy seCTor

Project description

Developing AI solutions for the energy sector

AI is essential for optimising, testing, and maintaining innovative energy solutions and the energy sector as a whole. However, despite its advantages, extensive testing and validation are necessary to ensure the safety and durability of these systems. This testing often requires expensive and difficult-to-access tools. The EU-funded AI-EFFECT project aims to improve access to the tools and facilities needed for developing, testing, and validating AI solutions in the energy sector. To achieve this, the project will establish a novel European testing and experimentation facility, composed of distributed, virtually connected existing European facilities. Additionally, it will develop a digital platform that provides interoperability, scalability, and flexibility for users and resources.

Objective

AI-EFFECT will establish a European Testing Experimentation Facility (TEF) for developing, testing, and validating AI applications in the energy sector. It will be distributed across nodes, virtually connecting existing European facilities. The solution includes a digital platform leveraging European building blocks for interoperability, flexibility, and scalability. AI-EFFECT aims to be a central hub for testing energy sector AI algorithms, fostering collaboration across utilities, industry, academia, and regulatory authorities. Resilience is ensured through a decentralized design, aligning with the EU Energy Data Spaces framework.
The project involves developing 4 use cases/nodes addressing key energy challenges, focusing on district heating, transmission congestion management, DERs integration, and energy communities. The framework involves utilities proposing challenges, vendors developing algorithms, and researchers contributing solutions. Each use case has evaluation criteria, baselines, and benchmarks. AI certification procedures, including interpretability and verification, will be implemented, and the evaluation process will be automated.
Benchmarks and certifications are publicly available, encouraging open-source contributions. The project breaks sector barriers, leveraging existing infrastructures and technologies for cross-sectoral collaboration. The platform enforces policies for data quality, integrity, and privacy, promoting controlled data sharing and collaboration. Secure APIs ensure controlled interactions, including risk and security assessments. The consortium explores certification, standardization, and quality requirements in line with the EU AI Act.
Governance and business models for the enduring AI-EFFECT will be examined, considering the EU AI Act. The consortium aims to make AI-EFFECT a sustained business beyond initial funding, seeking input from members, other TEFs, and regulatory authorities for the preferred model.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-IA - HORIZON Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL5-2024-D3-01

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Coordinator

EPRI EUROPE DAC
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 891 125,00
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 891 125,00

Participants (18)

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