AI EFFECT

In an era dominated by digital transformation, the integration of Artificial Intelligence (AI) into critical infrastructures (such as energy sector infrastructure), has emerged as a transformative technology with the potential to revolutionise efficiency, resilience, and sustainability. The motivations for adopting AI in critical domains range from operational optimisation, cost savings and to enhanced resilience measures, with the aim of decarbonising the energy sector in the coming decades. Moreover, in the dynamic landscape of the energy sector, the promotion of a widespread European strategic autonomy through leading the development of key digital technologies is vitally important. Artificial Intelligence (AI) is rapidly advancing and evolving in parallel to the rapid evolution of the energy sector. Aligning the rapid evolutions of the energy industry and AI in Europe will position all European stakeholders at the forefront of sustainable, safe and technologically advanced solutions.

The project aims to address:

• The need to link utilities that have data streams and datasets and major challenges to the AI industry and research communities who have the tools and capabilities to solve the utility challenges.
• The need for a consistent, standardised approach to the development of trustworthy AI for the energy sector.
• The need to standardise and certify solutions based on a security and risk framework, governed by law.

The project's ambition is to facilitate energy utilities to submit data with challenges and potential use cases to AI-EFFECT. Industry will have secured access to the data for training their models and testing their solutions via a defined methodology openly and transparently. It should be noted that the project for AI-EFFECT will not just support the actual co-development of AI/ML models and algorithms, it will also act as an enabler for utilities and the AI-centric industry to innovate and develop based on prioritised use cases in a transparent manner. Additionally, finding a delicate balance between leveraging sensitive data for insights and ensuring compliance with privacy regulations, addressing ethical considerations related to AI decision-making such as bias and unintended consequences, establishing standardized regulations and guidelines for consistent implementation, and determining liability and accountability in the event of AI-related incidents or failures.

AI-EFFECT will establish a European Testing Experimentation Facility (TEF) to enable the development, testing and validation of Artificial Intelligence (AI) applications, at various stages of development and maturity, for the energy sector. AI-EFFECT will be distributed across Europe and will virtually interconnect existing European physical computer and laboratory facilities. The delivered solution will incorporate a digital platform, which will leverage pre-existing digital European building blocks and capabilities to ensure interoperability, flexibility, scalability, secure data exchange, transparency, and trustworthy testing procedures by design.

For the scope of the project four demonstration nodes and use cases will be developed. For the enduring solution, industry-driven use cases can be collaboratively developed by utilities, researchers, and AI-centric industrial vendors, For each use case the related training datasets will be made available, anonymised, and augmented where appropriate, and synthetic datasets will also be generated to extend the data sources as well as create additional testing scenarios. Each use case will have criteria and metrics for evaluation, baselines for existing performance, and benchmarks for innovation and previously tested solutions. AI-EFFECT will also define and implement an AI testing methodology per use case, including AI interpretability, verification, and end-to-end certification. The testing and evaluation process will be as automated as possible.

Funding:

The project is funded by the Federal Ministry of Education and Research (BMBF) under the initiative “Secure Use of Quantum Communication in Applications.”

Partners:

Fördergeber:

Deutsche Forschungsgemeinschaft.

Projektpartner:

• EPRI Europe DAC (Project coordinator)
• Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência
• Danmarks Tekniske Universitet
• Technische Universiteit Delft
• Institut de Recherche Technologique SystemX
• Fraunhofer FIT
• RWTH Aachen
• IKIM LTD.
• Maynooth University
• DNV AS
• EnliteAI GmBH
• Watt-IS S.A.
• Cooperativa Eléctrica do Vale D'Este
• Bornholms Varme A/S
• ENEL GRIDS S.R.L
• E Distribucion redes Digitales (Affiliated entity)
• Hertie School
• Center Denmark
• Tennet TSO BV (Associated partner)