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Abstract In the context of energy systems, managing the complex interplay between diverse power sources and dynamic demands is crucial. With a focus on smart grid technology, continuously innovating artificial intelligence (AI) algorithms, such as deep learning, reinforcement learning, and large language model technologies, have been or have the potential to be leveraged to predict energy consumption patterns, enhance grid operation, and manage distributed energy resources efficiently. These capabilities are essential to meet the requirements of perception, cognition, decision‐making, and deduction in energy systems. Nevertheless, there are some critical challenges in efficiency, interpretability, transferability, stability, economy, and robustness. To overcome these challenges, we propose critical potential directions in future research, including reasonable sample generation, training models with small datasets, enhancing transfer ability, combining with physics models, collective generative pre‐trained transformer‐agents, multiple foundation models, and improving system robustness, to make advancing AI technologies more suitable for practical engineering.
Dong et al. (Thu,) studied this question.