ABSTRACT Smart energy management systems (EMS) are entering a phase of rapid transformation. Artificial intelligence (AI)—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—has become the computational backbone for real‐time forecasting, scheduling, and control of renewable‐rich power systems. Yet the long‐term efficiency, resilience, and social acceptance of these systems depend critically on human‐AI synergy: well‐structured roles for people to supervise, override, and enrich AI decisions. This review integrates recent literature on AI‐powered EMS for smart grids, buildings, microgrids, and isolated hybrid systems with emerging human‐in‐the‐loop (HiTL) paradigms that explicitly incorporate occupants, operators, and policy makers. It offers an evaluation of data‐driven forecasting, adaptive optimisation, and edge intelligence, and highlights research gaps in transparency, interoperability, and co‐optimisation of technical and human objectives. We show that well‐designed human–AI collaboration improves not only energy efficiency and renewable integration but also the robustness and trustworthiness of future energy systems.
An et al. (Thu,) studied this question.