The transition towards sustainable and low-carbon energy systems highlights the crucial role of buildings, microgrids, and local communities as pivotal actors in enhancing resilience and achieving decarbonization targets. The application of artificial intelligence (AI) is of paramount importance, as it enables accurate prediction, adaptive control, and optimization of distributed resources. This review surveys recent advances in AI applications for transactive energy (TE) and dynamic energy management (DEM), emphasizing their integration with building automation, microgrid coordination, and community energy exchanges. It also considers the emerging role of life cycle–based methods, such as life cycle assessment (LCA) and life cycle cost (LCC), in extending operational intelligence to long-term environmental and economic objectives. The analysis is grounded in a curated set of 97 publications identified through structured queries and thematic filtering. The findings indicate substantial advancement in methodological approaches, notably reinforcement learning (RL), hybrid model predictive control, federated and edge AI, and digital twin applications. However, the study also uncovers shortcomings in sustainability integration and interoperability. The paper contributes by consolidating fragmented research and proposing a multi-layered AI framework that aligns short-term performance with long-term resilience and sustainability.
Andrzej Ożadowicz (2025) studied this question.