EnerTEF
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01
Establish a Reference Architecture (RA) for an Open Interoperable Common Federated European-scale Energy AI TEF accessible to all the players of the energy ecosystem.
02
Establish a regulatory/legal/ethical compliance framework contributing to the effective implementation of the EU Artificial Intelligence Act (EU AI Act) in the development lifecycle of trustworthy AI-powered services.
03
Leverage local node-level energy infrastructures availability, energy stakeholders’ know-how and ENERSHARE Data Space Building Blocks to instrument an open, standardisable and Energy Data Space compliant interoperability and trust infrastructure for the adaptation and upscale of data-driven trustworthy AI-powered services and Apps.
04
Integrate, deploy, operate and maintain the Federated Common European-scale Energy AI Testing and Experimentation Facility (EnerTEF), facilitating regulatory sandboxes for supervised testing and experimentation in real environments.
10
countries
Germany, Italy, France, Greece, Netherlands, Luxemburg, Slovenia, Portugal, Spain, Sweden
5
nodes
TEF DSO Node, TEF EV Node, TEF BUILD Node, TEF RES Node, TEF TSO Node
3
satellites
TEF H2 Satellite, TEF IND Satellite, TEF DHN Satellite
--> Select a country to discover detailed information about ongoing pilot projects there.
August 2025
Testing services catalogue for AI solutions
November 2025
First wave of EnerTEF solutions
July 2026
Second wave of EnerTEF solutions versions
November 2026
Successful demonstration of solution in the nodes and satellites
February 2027
Final wave of EnerTEF solutions with full functional implementation
August 2027
Demonstration of EnerTEF solutions in facilities outside the consortium
October 2027
Attraction of funding schemes funding schemes & Design of Go-to-Market business plans
EnerTEF provides a detailed catalogue of testing and experimentation services for AI tools across different fields in the energy sector.
City of Athens
This AI service assigns a quantitative efficiency score to each municipal building by comparing its actual energy performance to similar buildings in the city. The score reflects how efficiently a building uses energy relative to its size, use, and category.
City of Athens
The PV Self-Consumption Optimization service uses AI to maximize the use of locally generated solar energy within municipal buildings. By analyzing real-time consumption, historical PV production and energy consumption data, it identifies optimal load-shifting strategies to increase solar self-consumption and reduce reliance on grid electricity. The service recommends adjustments in building operations and controllable loads to align energy usage with PV generation peaks. This helps municipalities lower energy costs, improve sustainability performance, and enhance building energy autonomy.
City of Athens
Building Energy Consumption Forecasting service uses historical energy bills, building characteristics, real-time data from smart meters and seasonal patterns to forecast electricity consumption for municipal buildings. Leveraging machine learning models, it provides accurate short-term and mid-term forecasts. This service helps municipalities anticipate energy needs, optimize procurement, and identify potential inefficiencies before they escalate, supporting smarter and more sustainable energy management across the building portfolio.
ELGO, ELES
Currently the DSOs have multiple requests for 5-20 MW battery energy storage system (BESS) on the 20kV feeder in the primary transformer station. There exists a potential of activating multiple disturbing elements at once, which in turn can affect the TSO network frequency and voltage. Flexibility management between TSO and DSO networks can be achieved by modelling the DSO network and simulate the effects of distributed energy resources (DER) and BESS on the DSO network and in turn on the TSO network. This data can be used in a system that notifies TSO operators about the potentially disturbing events or even the system itself actively controls the deployed BESSs.
EnerTEF is featured in a new episode of EPRI Current, EPRI’s flagship podcast exploring major innovations, challenges, and opportunities in the global energy sector. In this episode, host Samantha Gilman speaks with Elissaios Sarmas, Project Coordinator of EnerTEF and Senior Researcher at NTUA, and Sotiris Pelekis from ICCS, representing the EnergyGuard project.
The discussion focuses on what utilities can gain from shared AI infrastructure, with particular attention to the role of Testing and Experimentation Facilities in supporting interoperability, trust, scalability, and cost-efficient development across the energy ecosystem.
The episode also highlights the complementarity between EnerTEF, EnergyGuard, and AI-EFFECT, and reflects on how these initiatives contribute to a stronger and more reliable framework for AI adoption in the energy sector.
We also thank EPRI for recording the episode and the AI-EFFECT project for organising this collaboration.
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Europe’s energy transition is no longer only a technical challenge. It has become a strategic priority closely linked to resilience, security, and sovereignty.
At the BRIDGE General Assembly 2026 in Brussels, key representatives from policy, research, and innovation gathered to discuss the future of Europe’s energy systems, with a focus on digitalisation, interoperability, flexibility, and risk management in an increasingly complex energy landscape.
We are pleased to announce that EnerTEF Project Coordinator, Elissaios Sarmas, participated as a speaker in the high-level panel discussion on “The Role of AI in the Energy Grids”, held on 23–24 March 2026 at The Skyline, Brussels.
During the session, he contributed to the dialogue on how artificial intelligence can support the transformation of power systems, making them smarter, more adaptive, and more secure.
As part of his intervention, he presented practical applications and insights from the EnerTEF project, including:
EnerTEF’s participation in the BRIDGE General Assembly 2026 highlights the project’s commitment to advancing trustworthy AI solutions that strengthen Europe’s energy infrastructure and support the transition to a more resilient and intelligent energy system.
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Missed our joint webinar on AI Testing & Validation for the Energy Sector? The full recording is now available — explore how cross-border testing and validation of AI using real energy systems is shaping the future of trustworthy AI in the energy sector.
Watch it here
Coming Soon!