AI for Next-Generation Smart Energy: Forecasting, Control and Applications

Dear Colleagues,

The global transition toward low-carbon, digitalized, and highly flexible energy systems is reshaping the way energy is produced, converted, delivered, consumed, and managed. Future smart energy systems will no longer be limited to conventional power grids, but will increasingly involve the coordinated operation of electricity, heat, gas, hydrogen, transportation, buildings, industry, storage, and demand-side resources. This transformation introduces unprecedented complexity in system forecasting, planning, operation, control, and market coordination. Artificial intelligence provides a powerful set of methods for extracting knowledge from data, modeling complex system behaviors, supporting autonomous decision-making, and improving the efficiency, reliability, and sustainability of energy systems.

This Special Issue, “AI for Next-Generation Smart Energy: Forecasting, Control and Applications”, aims to provide a broad forum for recent advances in artificial intelligence methods and their applications across modern and emerging energy systems. The scope covers both methodological innovations and practical implementations, including machine learning, deep learning, reinforcement learning, generative AI, graph learning, transfer learning, federated learning, physics-informed AI, digital twins, large models, and hybrid data- and model-driven approaches. Contributions may address forecasting, optimization, monitoring, control, planning, operation, trading, asset management, resilience enhancement, and decision support across different temporal and spatial scales.

Topics of interest include, but are not limited to, renewable energy forecasting, load and demand prediction, intelligent energy management, microgrid and virtual power plant operation, distributed energy resource coordination, building and industrial energy optimization, electric vehicle integration, energy storage scheduling, multi-energy system coordination, electricity market analytics, carbon-aware operation, fault diagnosis, anomaly detection, cyber–physical security, and resilience-oriented control. Research on AI-enabled applications in hydrogen systems, integrated energy systems, smart buildings, transportation electrification, energy Internet, and urban energy infrastructures is also welcome.

The Special Issue particularly encourages studies that move beyond purely data-driven performance improvement and address the practical requirements of real-world energy systems, including interpretability, robustness, scalability, privacy protection, uncertainty quantification, physical consistency, computational efficiency, and field deployability. Review articles, original research papers, case studies, benchmark studies, and perspectives are all invited. By bringing together advances in AI theory, energy-system modeling, and engineering practice, this Special Issue seeks to promote a comprehensive understanding of how artificial intelligence can support the next generation of smart, secure, efficient, and sustainable energy systems.

Dr. Qi Wang
Dr. Yue Yang
Dr. Xiaochong Dong
Dr. Ligeng Li
Guest Editors

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• artificial intelligence
• smart energy systems
• sustainable energy transition
• energy system intelligence
• cyber–physical infrastructure