Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework
Sotiris Pelekis, Evangelos Karakolis, George Lampropoulos, Spiros Mouzakitis, Ourania Markaki, Christos Ntanos, Dimitris Askounis
TL;DR
This paper addresses the need for trustworthy AI in the EU energy sector by mapping the regulatory and ethical landscape (EGTAI, ALTAI, the AI Act, CEN-CENELEC, AI4EU, and SHERPA) and introducing E-TAI, a domain-specific methodological framework for EPES contexts. E-TAI embeds EGTAI/ALTAI principles, extends them with energy-domain guidelines, and provides an iterative evaluation procedure to manage risks across seven core requirements, including privacy, robustness, transparency, and fairness. The framework is demonstrated through its application in the I-NERGY project (9 pilots, 15 use cases, 19 TAI services) and is positioned for refinement as the AI Act finalizes and ISO/CEN standards evolve. Overall, the study offers a practical handbook for developers and operators to design, deploy, and assess AI services in energy while ensuring compliance, accountability, and environmental sustainability, with planned expansion via iTrust6G and ongoing standards alignment; future work includes formal integration of the final AI Act provisions and broader standardization.
Abstract
The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAI application framework, called E-TAI, tailored for energy applications, including smart grid and smart building systems. This framework draws inspiration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAI to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.