Theoretical and hardware breakthroughs have brought artificial intelligence (AI) under the spotlight. The increasing pressure of global warming significantly accelerates the development of low carbon renewable energy and energy storage systems. Typical AI techniques such as neural networks, fuzzy logic, expert systems, and evolutionary computations have brought an advancing frontier towards various engineering applications, providing powerful tools to increase the efficiency and effectiveness in modeling, control, complex optimization, state estimation, and fault diagnostics for low carbon renewable energy and energy storage systems. Moreover, more interesting and practical applications on low carbon renewable energy and energy storage systems are promising to be proposed by adopting the novel deep learning and other AI approaches and platforms.
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.
Theoretical and hardware breakthroughs have brought artificial intelligence (AI) under the spotlight. The increasing pressure of global warming significantly accelerates the development of low carbon renewable energy and energy storage systems. Typical AI techniques such as neural networks, fuzzy logic, expert systems, and evolutionary computations have brought an advancing frontier towards various engineering applications, providing powerful tools to increase the efficiency and effectiveness in modeling, control, complex optimization, state estimation, and fault diagnostics for low carbon renewable energy and energy storage systems. Moreover, more interesting and practical applications on low carbon renewable energy and energy storage systems are promising to be proposed by adopting the novel deep learning and other AI approaches and platforms.
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.
