We are pleased to announce that EnerTEF has been featured in a new open-access publication in Engineering Applications of Artificial Intelligence. The paper, titled “A federated Artificial Intelligence testing and experimentation facility for the European energy sector”, is authored by Elissaios Sarmas, Alexandre Lucas, Andrés Acosta, Ferdinanda Ponci, Pedro Rodriguez and Vangelis Marinakis and is available at:
The article presents EnerTEF, the Energy Testing and Experimentation Facility, a federated platform designed to accelerate the safe, transparent and trustworthy deployment of Artificial Intelligence in the European energy sector. EnerTEF addresses a critical gap in current AI development: the absence of large-scale, integrated and realistic testing environments that allow advanced algorithms to be assessed under operational conditions while meeting strict privacy, interoperability and regulatory requirements. The publication introduces a unified architecture enabling full-stack evaluation of intelligent energy systems, including forecasting, optimization, learning under data distribution shifts and federated learning across multiple geographically distributed sites. High-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing are at the core of the platform, ensuring transparent and compliant experimentation.
The paper demonstrates the value of EnerTEF through three real-world scenarios: short-term hydropower generation forecasting, coordination between distribution system operators and distributed energy resources, and real-time optimization of energy self-consumption in municipal buildings. The results show that EnerTEF enhances model robustness, improves cross-context generalizability and supports innovation across complex energy infrastructures. This publication highlights the impact and scalability of EnerTEF as a practical pathway for advancing AI solutions in the energy domain.
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