
In an era dominated by digital transformation, the integration of Artificial Intelligence (AI) in critical infrastructure, particularly in the energy sector, promises enhanced efficiency, resilience, and sustainability.
The Digitalisation of the Energy System – EU Action Plan2 underscores the pivotal role of data and AI in shaping a more efficient, resilient, and sustainable energy landscape. However, challenges such as high-quality data availability, interoperability, and AI system security must be addressed to prevent potential threats and maintain integrity of critical infrastructure.
Additionally, regulatory challenges arise, in particular with the first European regulation on Artificial Intelligence (EU AI Act3) focusing on privacy, ethical concerns, and liability.
In this context, there is a critical need for an infrastructure for testing AI applications for the energy sector in an independent, protected environment, replicating conditions similar to real life. There is also a need for to validate the AI models in operational environments, to bridge the gap between laboratory and real-life adoption and between industry and research communities and European energy sector utilities.

The AI-EFFECT project will enable energy utilities to submit data with specific challenges and use cases, granting the industry secure access for AI model training and solution testing through a transparent methodology. Beyond co-developing AI/ML models, AI-EFFECT fosters innovation by prioritizing use cases and introducing several groundbreaking advancements.
These include an end-to-end AI certification process with Shapley values-based interpretability, a scalable modular architecture using VILLASframework, physics-informed digital twins software components for data generation and augmentation, and use cases spanning the entire energy value chain, from system operations to consumer-centric solutions.
AI-EFFECT Vision & Ambition
The AI-EFFECT project (Artificial Inteligence Experimentaion FacilityFor the Energy seCTor) 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 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.
AI-EFFECT will establish a European Testing and Experimentation Facility (TEF) for AI applications in the energy sector, enabling development, testing, and validation at various stages. It will virtually connect existing European computer and lab facilities through a digital platform, ensuring interoperability, scalability, and secure data exchange.
The platform’s decentralized design will allow both direct and remote access to distributed nodes across the EU, aligned with the EU Energy Data Spaces framework. Four demonstration nodes will address key energy challenges, with datasets and synthetic data supporting the testing of use cases. AI-EFFECT will also implement an AI testing methodology with automated processes, data security, and IP protection. It will ensure compliance with the EU AI Act, fostering European collaboration and innovation through open-source algorithms and secure platforms.
The framework will facilitate the natural evolution of centers of excellence for AI associated with specific use cases across the EU. Virtual collaboration and interconnected nodes will break down geographic boundaries, ensuring knowledge and innovation is not confined by domain or location.
AI-EFFECT has four Primary Objectives
01. Develop sets of use cases
Develop sets of use cases of strategic importance to the EU energy sector, at four European research institution nodes. Develop a testing methodology for each use case.
02. Develop framework architecture
Develop and implement a modular, interoperable, and scalable framework architecture which leverages existing EU initiatives, such as: TEFs, Data Spaces, Digital Innovation Hubs (DIH).
03. Develop end-to-end solution
Develop and test the end-to-end AI-EFFECT solution, for interaction with the AI-EFFECT demonstration nodes and use cases.
04. Develop governance and business model
Develop the governance and business model for the enduring AI-EFFECT. Engage with European and global energy sector stakeholders, and asset owners about AI-EFFECT to curate use cases, training, and test data and to match concepts and ideas to viable datasets.
Expected Results
AI-EFFECT aims to establish large-scale Testing and Experimentation Facilities (TEFs) across Europe for AI technologies in the energy sector.
These facilities will integrate both physical and virtual environments where technology providers can test AI-based solutions. The project focuses on applying AI to solve energy challenges, improve grid efficiency, and support Europe’s clean energy transition.

Modular and Scalable Architecture for AI Testing
Creation of a modular, scalable node-based architecture and an accessible digital platform for testing AI applications in energy systems. This platform is designed to facilitate seamless integration and adaptability across various energy scenarios.

Framework for Testing and Validation
Development of a robust framework that supports testing and validation of AI applications. This framework is designed to be generalizable across the entire energy sector, ensuring comprehensive and reliable application testing.

Collaborative AI Demonstrations with Energy Utilities
Demonstration of AI-driven solutions through strategic collaboration with leading European energy utilities. These partnerships will showcase the practical implementation and benefits of AI innovations within real-world energy systems.

Open-Source Digital Architecture for AI Integration
Development of an open-source digital architecture aimed at connecting various AI testing facilities. This architecture promotes interoperability and collective growth of AI research and application within the energy industry.

Sustainable Governance and EU Policy Recommendations for TEFs
Formulation of long-term governance strategies and business models to establish self-sustaining Testing and Experimentation Facilities (TEFs). This includes policy recommendations tailored for the European Union to support continuous innovation and sustainability.

EU Data Integration
Digital platform for AI-AFFECT integrated with existing EU data spaces and digital systems with end-to-end testing and demonstration.