About this Research Topic
This Research Topic aims to bring together the state-of-the-art advances of AI variants including deep learning, machine learning, fuzzy logic, and evolutionary computations for solving emerging problems in low carbon renewable energy and energy storage systems. The submissions are encouraged to focus on but not limited to AI applications to renewable generation forecasting, power system scheduling, battery modeling and charging strategy, plug-in electric vehicles and power-trains scheduling, multiple types of energy storage devices and systems, condition evaluation and fault diagnosis for renewable and energy storage components and all relevant topics.
Some of the topics are shown below:
• Deep learning variants for low carbon renewable energy and energy storage systems;
• Neural networks for low carbon renewable energy and energy storage systems;
• Expert systems for low carbon renewable energy and energy storage systems;
• Fuzzy logic applications for low carbon renewable energy and energy storage systems;
• Evolutionary computation for low carbon renewable energy and energy storage systems;
• Optimal smart grid scheduling with integration of renewable energy generation;
• Intelligent coordination and control of electric vehicles;
• Multiple types of energy storage devices and systems;
• Condition evaluation and fault diagnosis for renewable and energy storage components.
Keywords: Renewable Energy, Energy Storage System, Battery, Artificial Intelligence, Deep Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.