The evolving energy sector is increasingly seeking AI solutions as the energy landscape has transformed significantly in recent years. Rapid advances in digitalisation and AI are accelerating this shift, pushing stakeholders to enhance their capabilities and embed AI into their operations and services. As organisations navigate this digital transformation, the demand for trusted AI expertise continues to grow. EnerTEF responds by developing a Data Space–compliant, centralised access marketplace that integrates multiple thematic nodes across Europe, enabling the co-creation of reliable, validated AI solutions and bridging the gap between energy stakeholders and AI experts.
EnerTEF central platform
Within the project, a central platform acts as a one-stop shop that connects energy stakeholders with AI experts. Energy stakeholders publish detailed descriptions of the services they need, together with the physical and digital assets available for experimentation, so AI providers can access relevant data, information and existing tools to develop solutions. High-performance computing resources and an AI Workbench are made available to AI providers through the federated nodes, enabling them to design, run, and evaluate experiments within the platform. All data is accessed through a dedicated Data Space, ensuring secure and transparent exchange and governance. In this way, EnerTEF establishes a complete energy and AI ecosystem where stakeholders can connect, define collaborations and transactions, express business needs, and validate AI services, laying the groundwork for a centralised European energy marketplace.
Nodes and satellites
EnerTEF will be implemented in phases, starting with an initial operational network that demonstrates the platform’s capabilities in real conditions. The first phases focus on validating the approach, processes, and services with a core group of facilities and stakeholders. The results will support the platform’s wider commercial rollout and onboarding of additional participants. In this initial deployment, EnerTEF is structured around the following TEF nodes and satellites:
TEF RES
In the TEF RES node, which leverages sites in Greece and Portugal, the focus is on harnessing diverse datasets from renewable energy facilities. These include hydropower plants, PV installations, wind parks and offshore renewables, including wave energy generation. Asset owners use the node to express needs that AI providers can address, such as generation forecasting, predictive maintenance, fault detection, monitoring, and operational optimisation. To accelerate this work, existing solutions and assets originating from previous EU projects, including AI-based forecasting and time series analytics tools, are integrated into the EnerTEF marketplace, supporting experimentation and helping validate and compare service results.
TEF EV
Building on an energy community in Luxembourg, EV node develops and validates services around a network of electric vehicle (EV) chargers. EVs are becoming a key asset within energy communities because they introduce flexibility that can support local balancing, reduce peaks, and increase the use of locally produced renewable energy. The community also integrates complementary assets such as PV systems, wind parks, and battery storage, enabling integrated scenarios. Priority services include EV flexibility, optimising charging against renewable generation and available battery capacity, AI-enhanced multi-agent systems for Vehicle-to-Grid applications and EV-driven demand forecasting.
TEF TSO
Hosted in Slovenia, the TEF TSO node aims to enhance the utilisation of existing equipment and resources by enabling structured collaboration with AI experts. Its physical and virtual backbone is the ELES Diagnostics and Analytics Centre, which brings together big data capabilities, advanced analytics and strong technical expertise. ELES and Elektro Gorenjska act as the end users, representing the TSO and DSO respectively and actively contribute to the validation, demonstration, and fine-tuning of the developed AI services. Priority services in this node focus on power management, fault detection and identification, and transmission grid stability assessment.
TEF DSO
The TEF DSO node focuses on developing AI solutions that support distribution grid services for monitoring, control, and automation. Based in Germany, it leverages a real-time digital simulation environment that can generate high-quality synthetic data, enabling rapid development, testing, and validation of services even when field data access is constrained. This setup supports use cases such as grid state estimation and anomaly detection, fault localisation, predictive maintenance and operational optimisation, helping DSOs evaluate AI tools in a controlled and reproducible setting.
TEF BUILD
The TEF BUILD node in Greece supports the digitalisation and energy transition of the Municipality of Athens by leveraging building information, energy data and local assets such as PV systems and EV chargers. The focus is on enabling a smarter, more decarbonised municipality and helping facility managers make better decisions that deliver measurable energy and financial savings. The most needed services include AI-driven building consumption optimisation, monitoring and fault detection, predictive maintenance, and building self-consumption maximisation.
Hydrogen, industry, and district heating network satellites
The three smaller satellite nodes, based in France, Greece and Spain, focus on AI services for hydrogen energy systems, industry, and district heating and cooling networks, respectively.
The TEF Hydrogen node supports the development of AI-driven energy management, control, and predictive maintenance services, with emphasis on applications in fuel cell hybrid electric vehicles and hydrogen-enabled microgrids. The TEF Industry node targets AI solutions for process planning optimisation and sustainable supply chains, including improved production scheduling and manufacturing process modelling. The TEF District Heating Network node prioritises demand forecasting, energy consumption optimisation and operational decision support, building on existing DHN physical infrastructure and digital twins that provide the virtual environment and data needed for experimentation and validation.
Impact on the energy sector
EnerTEF accelerates Europe’s energy transition by turning real stakeholder needs into trusted, validated AI services. It reduces time to deployment, strengthens interoperability and cybersecurity and enables cross-border collaboration, helping AI solutions scale across the energy value chain. It also creates a repeatable pathway for broader adoption by integrating new stakeholders, nodes and market-ready services over time.
Please note, this article will also appear in the 25th edition of our quarterly publication.