Applications of Artificial Intelligence in Sustainable Energy and Environment

Dear Colleagues,

Artificial intelligence (AI) is accelerating the transition toward cleaner, safer, and more resilient energy and environmental systems. From forecasting renewable generation and orchestrating distributed resources to monitoring air and water quality at scale, AI enables data‑driven decisions that improve efficiency, reliability, and sustainability. At the same time, advances in edge computing, digital twins, physics‑informed learning, and privacy‑preserving analytics are making AI deployable in real‑world infrastructure.

This Topic invites original research articles, reviews, short communications, case studies, and data/resource papers that demonstrate AI‑enabled methods, tools, and applications that advance sustainable energy and environmental stewardship. Submissions that report field validation, open datasets/code, reproducible pipelines, uncertainty quantification, and explainable decision support are especially encouraged.

Topics of interest include, but are not limited to:

• AI for renewable energy forecasting (solar irradiance, wind speed, hydro resources);
• Predictive maintenance, fault detection, and diagnostics for PV plants, wind turbines, inverters, and other assets;
• Optimization and control of smart grids, microgrids, and virtual power plants (DER coordination, demand response, EV/V2G scheduling);
• Energy storage analytics and control (SoC/SoH estimation, degradation modeling, thermal management, hybrid storage);
• Building and campus energy management, HVAC optimization, occupancy‑aware control, and digital twins;
• Power electronics and converter control enhanced by machine learning and reinforcement learning;
• Edge AI, IoT sensing, and federated learning for privacy‑preserving energy analytics;
• Physics‑informed and hybrid AI models that integrate domain knowledge with data‑driven approaches;
• Uncertainty quantification, interpretability, safety, and robustness of AI in critical energy infrastructure;
• AI-based risk early warning and emergency control for power systems with high penetration of renewable energy;
• Energy market analytics and carbon/energy trading (price forecasting, bidding strategies, risk management);
• Planning for decarbonized systems: renewable siting, transmission expansion, and multi‑energy systems design using AI;
• AI for hydrogen and fuel‑cell systems (electrolyzer optimization, diagnostics, system integration);
• Electric mobility: smart charging strategies, fleet optimization, and EV–grid interaction;
• Environmental monitoring and protection using AI (air and water quality, emissions estimation, remote sensing, wildfire/flood risk);
• AI‑assisted life‑cycle assessment, circular‑economy strategies, waste‑to‑energy systems, and carbon footprint analysis;
• Carbon capture, utilization and storage (CCUS) modeling, monitoring, and optimization;
• Water–energy–food nexus modeling and resource allocation with AI;
• Generative AI and large language models for engineering knowledge management, anomaly summarization, and decision support;
• Benchmark datasets, standardized evaluation protocols, and reproducible MLOps for energy and environmental AI;
• Policy, ethics, equity, and societal impacts of AI‑enabled sustainable energy and environmental decision‑making.

Prof. Dr. Junhua Zhao
Prof. Dr. Yanbo Chen
Topic Editors