Artificial Intelligence Supporting Sustainable Energy Management: A Comparative Case Study of European Projects
Purpose: The aim of this article is to comparatively analyse the use of artificial intelligence (AI) in selected European projects related to energy management and to develop an analytical framework for classifying AI applications according to their functions, the types of data used, associated technologies and digital components, and the level of AI application. Design/Methodology/Approach: The study employed a qualitative comparative case study analysis of three European research and innovation projects: DATAWiSE, BD4NRG, and TwinEU. The analysis was based on publicly available secondary data, primarily project documentation and institutional sources. Findings: The analysis identified three distinct configurations of AI application. DATAWiSE represents AI application at the building level, BD4NRG at the level of services and the energy data ecosystem, and TwinEU at the level of energy system infrastructure. The projects differ in terms of AI functions, the types of data used, and associated digital technologies. The results show that similar AI functions may occur in different management and technological contexts and should therefore be considered within the broader data and technological environment. Practical Implications: The proposed framework may support the systematic comparison of AI applications in energy-related projects by identifying relationships between AI functions, the data environment, associated digital technologies, and the level of application. It may also provide a structured basis for analysing further European energy projects. Originality/Value: The study proposes a multidimensional analytical framework combining AI functions, the type of data used, associated technologies and digital components, and the level of AI application. It also distinguishes between the use of AI in sustainable energy management processes and the demonstrated sustainability outcomes of its application. Given the exploratory nature of the study, the framework is intended to organise and compare the analysed cases rather than provide a universal typology of AI applications in the energy sector.