EnerTEF

The Future of Energy through AI Testing and Experimentation

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Our Vision

Reference Architecture

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.

Compliance Framework

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.

Energy Hub

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.

EnerTEF Integration

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.

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Testing Our Innovations in Action

countries

Germany, Italy, France, Greece, Netherlands, Luxemburg, Slovenia, Portugal, Spain, Sweden

nodes

TEF DSO Node, TEF EV Node, TEF BUILD Node, TEF RES Node, TEF TSO Node

satellites

TEF H2 Satellite, TEF IND Satellite, TEF DHN Satellite

--> Select a country to discover detailed information about ongoing pilot projects there.

Roadmap

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

Services Catalogue

EnerTEF provides a detailed catalogue of testing and experimentation services for AI tools across different fields in the energy sector.

AI-Based Hydrogen Leak Detection and Localization

UTBM

The service is intended to be evaluated using historical datasets containing both normal operation and leak scenarios. The evaluation framework would separate training and testing data to ensure a robust assessment of detection and localization performance. Performance is expected to be measured using standard classification metrics, including accuracy, precision, recall, F1-score, and false alarm rate for leak detection, along with localization accuracy for identifying the leak position. Visual analysis of sensor signals and detected events would further support validation of the model’s ability to capture abnormal behaviour.

Monitoring & Anomaly Detection

AI-Based Multi-Energy Demand Forecasting (Electricity and Hydrogen)

UTBM

The Multi-Energy Demand Forecasting service delivers AI-driven forecasts of electricity and hydrogen demand using time-series data from integrated energy systems. Based on advanced deep learning architectures (LSTM, TCN, Transformers), the service models interdependencies between electricity and hydrogen demand across coupled systems such as microgrids, industrial facilities, and energy hubs, supporting operational planning and resource allocation.

Predictive & Prescriptive Analytics

Operational Scheduling for DHN

Veolia

The Operational Scheduling for DHN service generates optimal operational schedules for the district heating network by translating demand forecasts and real-time data into actionable control strategies. Using forecast-driven optimisation combined with rule-based and data-driven techniques, the service recommends supply temperature setpoints, load distribution plans, and operation timelines to maximise energy efficiency and minimise operational costs.

Predictive & Prescriptive Analytics
Optimization & Decision Support

Anomaly Detection and Fault Diagnosis in DHN

Veolia

The Anomaly Detection and Fault Diagnosis service detects abnormal patterns in the Torrelago district heating network using AI-based techniques applied to real-time and historical data. By establishing expected behavioural baselines from historical patterns and continuously comparing real-time data against these baselines, the service provides early warnings of inefficiencies, faults, and unexpected operational conditions.

Monitoring & Anomaly Detection

Visit the full Catalogue

News & Events

2nd TEF Joint Webinar!

From Experimentation to Deployment: Aligning AI Testing Services with Industry Needs

EnerTEF, EnergyGuard and AI-EFFECT are joining forces for a 90-minute webinar on how AI Testing and Experimentation Facilities can support the deployment of AI-driven solutions in the energy sector.

EnerTEF is pleased to join forces with EnergyGuard and AI-EFFECT for a dedicated webinar exploring how AI Testing and Experimentation Facilities can support the transition from research and experimentation to real-world deployment in the energy sector.

The webinar will take place on Wednesday, 17 June 2026, at 11:00 AM CEST, bringing together energy innovators, startups, SMEs, researchers and industry stakeholders for a focused 90-minute session.

Moving an AI solution from a laboratory environment into a live power network is a major technical challenge. This webinar will highlight how Europe’s network of Testing and Experimentation Facilities can help bridge this gap by providing access to specialised testing services, infrastructure and validation environments.

During the event, participants will gain insights into the services offered by EnerTEF, EnergyGuard and AI-EFFECT, and learn how these initiatives can support the development, testing and deployment of AI-driven energy solutions under realistic operational conditions.

Participants will also hear from experts during an Industry-TEF roundtable discussion focused on the practical needs, challenges and opportunities facing the energy technology community.

1)Learn how AI testing services can support the deployment of energy technologies.

2)Discover specialised testing environments tailored to the needs of the energy sector.

3)Understand how TEFs can help innovators validate AI tools under realistic conditions.

4)Gain practical insights into market readiness, compliance and data governance.

5)Explore opportunities for cross-TEF collaboration across Europe.

Register here: https://events.teams.microsoft.com/event/5a675226-1200-4881-9a7f-544fa88ed9a4@32cc6400-153d-42f7-bc55-77e5a50f1bcf

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EnerTEF at EUSEW 2026: Advancing AI-powered grids for Europe’s energy future

EnerTEF will take part in the European Sustainable Energy Week 2026, joining key discussions and activities dedicated to the future of smart, secure, and digital energy systems in Europe.

This year’s EUSEW, taking place from 9–11 June 2026 in Brussels and online, will bring together policymakers, researchers, industry representatives, and energy stakeholders to explore solutions for accelerating Europe’s clean energy transition.

Join the session: AI-powered grids: securing the future of European energy

EnerTEF will be represented in the session “AI-powered grids: securing the future of European energy”, which will explore the challenges and opportunities linked to the development, deployment, and mastery of sovereign European AI solutions for electricity grids.

The session will focus on how AI can support the energy transition by enabling smarter, more resilient, and more secure grid operations.

Session details
Date: Wednesday, 10 June 2026
Time: 15:30–17:00
Venue: NH Berlaymont – Jean Rey, Brussels

The discussion will address key themes including the EU and global decarbonisation framework, grids, and digitalisation.

Speakers include:

Elissaios Sarmas, Senior Research Associate, National Technical University of Athens (EPU-NTUA)
Nathalie Samovich, Steering Board Member, Alliance for AI, IoT and Edge Continuum Innovation (AIOTI)
Antonello Monti, Professor, Fraunhofer FIT
Marie-Sophie Debry, Power System Stability R&D Director, Réseau de Transport d'Electricité (RTE)
Michael Metzger, Distinguished Engineer on Sustainable Energy and Infrastructure, Siemens Energy
Gianluca Lipari, Project Manager, EPRI Europe

Meet EnerTEF at the Smart Energy Cluster stand

EnerTEF will also hold a full 3 - day stand under #SmartEnergyCluster!

The Smart Energy Cluster is an initiative currently managed by IEECP and NTUA under the Business2Act project. For this occasion, our stand will bring together 19 EU-funded initiatives working across smart grids, building decarbonisation, energy efficiency, and digital innovation. Visitors will have the unique opportunity to interact with experts and explore deliverables from a powerhouse of projects: BUILDON, BUILD-OSS, BUSINESS2ACT, EnerTEF, Enpower, ESCALATE, EU-TRACE, EVELIXIA, GiDomus, GINNGER, Hycool_IT, LEG-UP, LiveBetter, PVSmile, ReLIFE, REN+HOMES, Reschool, WILSON, and WeForming.

Whether you are walking the exhibition floor in Brussels or tracking the updates online, make sure the Smart Energy Cluster is on your agenda. Participation is free of charge, but registration is mandatory.

👉 Secure your EUSEW 2026 pass and bookmark the Smart Energy Cluster Stand here!

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AI-EFFECT Podcast with EnerTEF & Energy Guard Projects - Recorded by EPRI

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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