About this Research Topic
Energy research is a upcoming and important topic for human wellbeing and society. Sustainable energy development has been attracting more and more attention all over the world. Computational intelligence as one kind of effective and sophisticated method has been widely applied to various fields, such as engineering, economics, optimization, and energy systems. The development of energy can rely on computational intelligence methods to achieve complex modeling, simulation, prediction, and optimization. Compared with traditional methods, computational intelligence methods, such as evolutionary computation and machine learning, show great value and improved results in energy fields. Thus, the combination of computational intelligence and energy system modeling can produce significant contributions to developing advanced technologies and applications in energy domains.
Computational intelligence methods can improve energy models for efficiency, simulation, and storage in energy systems, especially for large-scale, dynamic, nonlinear, and complex systems. It is of great interest to investigate the role and significance of computational intelligence methods in modeling and optimizing sustainable energy systems, such as evolutionary computation, artificial neural networks, metaheuristic algorithms, and machine learning. Sustainable energy systems can involve wind energy, power energy, clean fuel energy, solar energy, and hydraulic energy.
This Research Topic aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning computational intelligence and sustainable energy systems, in particular, to the integration between academic research and industry applications, and to stimulate further engagement with the user community. This Research Topic also aims at presenting the latest developments in computational intelligence methods for sustainable energy systems, as well as exchanging new ideas and discussing the future directions of computational intelligence for energy domains. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of sustainable energy systems are very welcome for this Research Topic.
Potential topics include, but are not limited to:
(1) High-dimensional and many-objective evolutionary algorithms for energy systems
(2) Metaheuristic algorithms for energy systems
(3) Deep learning for large-scale optimization
(4) Novel artificial neural networks for prediction
(5) Parameter optimization of energy models
(6) Simulation of computational models for energy systems
(7) Optimization of energy efficiency
(8) Modeling of complex energy systems
Computational intelligence methods can improve energy models for efficiency, simulation, and storage in energy systems, especially for large-scale, dynamic, nonlinear, and complex systems. It is of great interest to investigate the role and significance of computational intelligence methods in modeling and optimizing sustainable energy systems, such as evolutionary computation, artificial neural networks, metaheuristic algorithms, and machine learning. Sustainable energy systems can involve wind energy, power energy, clean fuel energy, solar energy, and hydraulic energy.
This Research Topic aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning computational intelligence and sustainable energy systems, in particular, to the integration between academic research and industry applications, and to stimulate further engagement with the user community. This Research Topic also aims at presenting the latest developments in computational intelligence methods for sustainable energy systems, as well as exchanging new ideas and discussing the future directions of computational intelligence for energy domains. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of sustainable energy systems are very welcome for this Research Topic.
Potential topics include, but are not limited to:
(1) High-dimensional and many-objective evolutionary algorithms for energy systems
(2) Metaheuristic algorithms for energy systems
(3) Deep learning for large-scale optimization
(4) Novel artificial neural networks for prediction
(5) Parameter optimization of energy models
(6) Simulation of computational models for energy systems
(7) Optimization of energy efficiency
(8) Modeling of complex energy systems
Keywords: Artificial intelligence, Evolutionary computation, Artificial neural networks, Machine learning, Metaheuristic algorithms, Energy systems
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.
