Artificial Intelligence in the Energy Sector: A Review of Common Applications
Abstract
Artificial intelligence (AI) is increasingly being applied across the energy sector to support forecasting, diagnosis, optimization, control, and system planning. This review synthesizes common applications of AI in electricity-centered energy systems, including load and renewable-energy forecasting, electricity-market forecasting, grid operation and control, demand response and building energy management, predictive maintenance and fault diagnosis, cybersecurity and anomaly detection, battery and electric-vehicle management, industrial energy efficiency, and energy-system planning. The review examines major AI and machine-learning approaches and emphasizes their suitability for different energy-sector decision problems. Particular attention is given to practical deployment considerations, including data quality, time-aware validation, uncertainty, physical and operational constraints, explainability, cybersecurity, latency, distribution shift, and post-deployment monitoring. Emerging directions such as physics-informed and hybrid AI, graph neural networks, federated learning, generative and foundation models, and digital twins are also discussed. The review highlights the importance of integrating AI with engineering knowledge, physical constraints, rigorous validation, and human oversight to support reliable and responsible deployment in energy systems.
