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

EnerTEF at the NTUA Researchers’ Night 2026

EnerTEF will participate in the Researchers’ Night 2026, organised by the National Technical University of Athens (NTUA) on Friday, 25 September 2026, at the Zografou Campus in Athens.

From 17:00 to 23:00, NTUA will open its doors to the public as part of the European Researchers’ Night, bringing research, science and innovation closer to citizens and offering visitors the opportunity to meet researchers, discover new technologies and explore ongoing research activities.

EnerTEF will be there with its own interactive stand, inviting visitors to discover how Artificial Intelligence can support the transformation of the European energy sector.

At the EnerTEF stand, visitors will have the opportunity to:

discover what EnerTEF the Common European-scale Energy AI Federated Testing and Experimentation Facility is and how it supports the testing and validation of AI solutions for energy;
learn more about the project’s testing facilities, services and activities across Europe;
interact with the EnerTEF team and discuss the role of AI in areas such as smart grids, renewable energy, buildings and electromobility;
take part in an interactive dashboard game, offering a hands-on and engaging way to explore energy-related data and concepts.

EnerTEF at AI in Energy 2026!

As part of AI in Energy 2026, EnerTEF will organise a Capacity Building Workshop on Monday, 5 October 2026, from 15:00 to 16:30 (Athens time / EEST) at Serafio, Athens.

The workshop will focus on how the EnerTEF Portal and the project’s testing and experimentation facilities can support startups and SMEs in developing, testing and validating AI-based services for the energy sector. Special emphasis will be placed on TEF BUILD and TEF IND, highlighting opportunities for companies working in areas such as buildings, industry, energy efficiency, flexibility, e-mobility and digital energy services.

The session will explore the needs of end users, demonstrate how innovative companies can engage with the EnerTEF ecosystem, and present how startups and SMEs can benefit from access to infrastructure, data, experimentation environments and support services. It will also provide an opportunity to better understand how AI solutions can move from concept and prototype to testing in real or near-real operating conditions.

Participants will have the chance to learn more about:

the EnerTEF Portal and how to access it,
the opportunities offered by TEF BUILD and TEF IND,
the needs and expectations of end users,
and how SMEs and startups can develop and test their own AI-enabled energy services.

The workshop is organised in the framework of AI in Energy 2026, co-organised by HAEE and DSS Lab / EPU-NTUA.

Language: The event will be conducted in greek

Registration:
https://1staiinenergy.eventsadmin.com/Register

EnerTEF at LF Energy Summit Europe 2026: From Research to Market

Tuesday, 15 September 2026
11:05–11:35 CEST
Crossover, LF Energy Summit Europe 2026, Berlin

EnerTEF will be represented at LF Energy Summit Europe 2026 in Berlin, where Elissaios Sarmas, Senior Researcher at EPU-NTUA, will participate in the panel session “From Research to Market: The Challenge Ahead for Research and Open Source.”

The session will explore one of the key challenges facing research and innovation projects: how promising technologies can move beyond the research environment and become solutions that are accessible, scalable and sustainable in real-world energy systems.

Elissaios will join Antonello Monti, RWTH Aachen University; José Ricardo Andrade, INESC TEC; Geethu Joseph, CRESYM; and Ferdinando Bosco, Engineering Ingegneria Informatica for a discussion combining short presentations with an expert panel on open-source technologies, exploitation pathways and the transition from research results to market-ready solutions.

As part of the discussion, Elissaios will present the EnerTEF approach to bringing AI-based energy services from development and experimentation towards real-world testing and wider deployment.

A central element of this process is the EnerTEF Portal, which provides a common access point to the services and testing capabilities developed across the EnerTEF ecosystem. Through EnerTEF’s Nodes and Satellites, AI solutions can be tested and validated in representative energy environments before becoming accessible to a broader community of innovators, researchers, SMEs and energy stakeholders.

👉 Explore the EnerTEF Portal: https://portal.enertef.eu/home

The presentation will also highlight TwinREV, developed in the context of CRETE VALLEY by NTUA with the support of RWTH Aachen University and INESC TEC.

TwinREV is a digital platform designed to monitor, forecast and optimise local energy flows for Energy Communities by integrating a suite of tools, services and simulation engines. It also provides grid operators with insights and simulations that support better energy decision-making.

Powered by cloud computing, machine learning, real-time power-flow simulations and flexibility indicators, the platform demonstrates how research results can be transformed into practical tools supporting local energy systems and communities.

👉 Explore TwinREV: https://twin-rev.epu.ntua.gr/

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