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

UTBM Presented EnerTEF H₂ Satellite at GSET 2026

On 9 to10 July 2026, the Workshop on Green and Sustainable Energy Technologies (GSET 2026) took place at the Innovation Crunch Lab of the Université de Technologie de Belfort-Montbéliard (UTBM) in Belfort, France.

The workshop brought together around 20 invited researchers, professors and experts from the United Kingdom, the United States, Canada, Italy, Australia, China and France. Participants exchanged knowledge and discussed recent advances in green and sustainable energy technologies, with a strong focus on international research cooperation and future collaborative opportunities.

During the event, EnerTEF partner UTBM presented the TEF H₂ Satellite, introducing its hydrogen testing facilities, AI-driven services and the opportunities it offers to researchers and innovators.

The presentation highlighted how the EnerTEF H₂ Satellite supports the testing, validation and development of innovative hydrogen technologies within a controlled, research-oriented environment. Through its specialised infrastructure and services, the Satellite contributes to the development of trustworthy and advanced AI-enabled solutions for the hydrogen sector.

Workshop Agenda

Thursday, 9 July 2026

09:15 - Registration and opening remarks
09:30 - 12:00 - Session 1: Presentations and discussion
12:00 - 14:00 - Lunch break
14:00 - 17:15 - Session 2: Presentations and discussion
17:30 - 18:00 - Visit to the UTBM hydrogen platform
19:00 - Gala dinner at the Citadelle restaurant in Belfort

Session 1 included presentations on high-temperature fuel cells, the EnerTEF H₂ Satellite, large-scale proton exchange membrane fuel cells, offshore wind-powered hydrogen production, autonomous microgrids and vehicle-to-grid technologies.

Session 2 addressed sustainable innovation, converter fault diagnosis, battery safety and degradation, high-power-density fuel cells, hybrid hydrogen-wind systems and hydrogen energy storage for automotive and microgrid applications.

Friday, 10 July 2026

09:30–12:00 - Session 3: Presentations and discussion
12:00–14:00 - Closing session and networking lunch
14:00–19:00 - Excursion to Colmar

The final session covered fuel-cell hybrid electric vehicles, carbon capture and mineralisation, real-time modelling and hardware-in-the-loop testing, DC microgrids, fuel-cell system modelling and the use of green hydrogen for net-zero chemical manufacturing.

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EnerTEF Supports Special Session on AI for the Future Energy System at ICCP 2026

EnerTEF is pleased to support the special session AI for the Future Energy System, as part of the European AI Innovation Month.

Date: 23 October 2026
Location: Cluj-Napoca, Romania
Event: IEEE ICCP 2026

The session will be organised by the Technical University of Cluj-Napoca, the Artificial Intelligence Research Institute – AIRi@UTCN, and DSS Lab, EPU-NTUA, alongside the 2026 IEEE 22nd International Conference on Intelligent Computer Communication and Processing, held from 22-24 October 2026.

Bringing together researchers, engineers and industry practitioners, the session will explore how advanced artificial intelligence methods can contribute to smarter, more resilient and sustainable energy systems. Key areas will include electricity grids, energy markets, renewable energy integration and AI-enabled energy management.

The session is closely connected to the work of EnerTEF, a pan-European initiative developing a federated testing and experimentation facility for trustworthy AI solutions in the energy sector.

EnerTEF enables AI innovators to test, validate and demonstrate energy-related technologies in realistic environments, supporting their progression from research and development towards practical deployment and market adoption.

The session also aligns with related European research and innovation initiatives, including:

Through this collaboration, EnerTEF aims to strengthen knowledge exchange between researchers, technology providers, energy stakeholders and the wider European AI community.

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EnerTEF Final Services Catalogue is Now Available

The EnerTEF Final Services Catalogue is now available online, presenting 65 AI-based services designed to support the energy transition across buildings, renewables, electric vehicles, grids, hydrogen, district heating, and industry.

The catalogue provides startups, SMEs, technology providers, and energy stakeholders with a clear overview of the services available within EnerTEF. Through these services, SMEs can explore opportunities for testing and experimentation, validate innovative AI-based solutions, and contribute to the development of trustworthy AI applications for the energy sector.

The services cover areas such as forecasting, optimisation, flexibility, anomaly detection, predictive maintenance, energy efficiency, grid intelligence, and renewable energy integration.

Explore the Services Catalogue

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