Abstract:

With the rapid development of Artificial Intelligence (AI), its applications in energy systems are expanding, from intelligent scheduling and fault diagnosis to energy management and optimization, driving the transition toward smarter, low-carbon, and more efficient energy infrastructures. However, this transformation faces multiple challenges, including data quality and security issues, lack of model interpretability, insufficient coordination between technology and regulation, and delays in ethical and policy responses. This paper systematically reviews key AI application scenarios in energy systems, analyzes core technological and governance constraints in the transformation process, and proposes countermeasures from the perspectives of technology, institutions, and policy. The goal is to provide theoretical support and practical strategies for building intelligent, efficient, and sustainable future energy systems.

Key words: artificial intelligence, energy systems, intelligent transformation, data governance, green and low-carbon transition