@article{Huang2026, 
author = {Tao Huang and Jingsi Chen and Jun Yin and Michela Meo},
title = {Artificial Intelligence Applications in Energy Systems: A Comprehensive Review},
year = {2026},
journal = {Power and Energy Future},
volume = {1},
number = {1},
pages = {9650001},
keywords = {Artificial intelligence, energy systems, machine learning, deep learning, reinforcement learning, large language models},
url = {https://www.sciopen.com/article/10.26599/PEF.2025.9650001},
doi = {10.26599/PEF.2025.9650001},
abstract = {The global energy sector is transforming under decarbonization, decentralization, and digitalization, introducing complexity and volatility into system operations, and challenging the limits of traditional analytical methods. Artificial intelligence (AI) encompassing machine learning, deep learning, and reinforcement learning has emerged as a critical enabler for building the next generation of energy infrastructure. This review provides a systematic assessment of AI’s role across key domains including forecasting, optimization, control, and maintenance. It also examines the emerging potential of large language models from several application perspectives. Our analysis identifies a clear trend from data-driven models toward more sophisticated hybrid and physics-informed architectures. We document this progression through specific advances in forecasting electricity load, renewable generation, and electricity price, in optimizing grid scheduling, energy storage, and demand-side resources, and in enhancing system resilience through intelligent control and maintenance. Beyond current applications, we discuss future research challenges: developing trustworthy AI for high-stakes environments, creating scientific AI that integrates physical principles, managing complex sector-coupled systems, and progressing toward generalized energy foundation models. Overall, this review offers a structured understanding of the synergy between AI and energy systems, outlining current capabilities and the governance required for trustworthy and equitable deployment.}
}