About this Research Topic
Regarding the population growth, industrialization, environmental issues due to consumption of fossil fuels, it is crucial to develop the utilization of clean energy systems. Renewable energy technologies have been developed in recent decades to supply energy for different purposes such as cooling, heating, electricity production and desalination. Depending on the applications, several technologies such as solar collectors, geothermal power plants, wind turbines, solar photovoltaic cells etc can be employed. In addition, hybrid energy systems, composed of two or more technologies, could be used to improve the reliability and overall performance of the systems. Due to the performance dependency of these systems on several factors such as the weather condition, applied technology, employed material etc, their modeling and optimization can be complex.
Regarding the complexities of the renewable energy systems, it is crucial to apply powerful tools for modeling and optimization. Intelligent approaches such as artificial neural networks, support vector machines and other novel machine learning methods would have shown their ability in accurate modeling of these systems. Furthermore, the intelligent methods would be applied for modeling the components and properties of the materials used in renewable energy technologies. In addition, in term of optimization, there are several intelligent algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and the hybrid approaches, by combining two or more algorithms, would be useful to optimize the renewable energy systems based on technical, economical and environmental criteria.
The present Research Topic focuses on the applications of intelligent methods in modeling and optimization of renewable energy systems. In this regard, original papers with novel ideas on the modeling and optimization of different renewable energy systems are welcome to be submitted to this issue. In addition to original articles, high quality technical notes and review papers will be considered for possible publication. The main subjects -but not limited to- of this issue are as follows:
• Utilization of data-driven methods in modeling renewable energy systems,
• Optimization of renewable energy technologies by applying intelligent algorithms,
• Modeling the properties of the materials used in renewable energy technologies by applying data driven methods,
• Developing novel approaches for modeling and optimization of renewable energy systems.
Regarding the complexities of the renewable energy systems, it is crucial to apply powerful tools for modeling and optimization. Intelligent approaches such as artificial neural networks, support vector machines and other novel machine learning methods would have shown their ability in accurate modeling of these systems. Furthermore, the intelligent methods would be applied for modeling the components and properties of the materials used in renewable energy technologies. In addition, in term of optimization, there are several intelligent algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and the hybrid approaches, by combining two or more algorithms, would be useful to optimize the renewable energy systems based on technical, economical and environmental criteria.
The present Research Topic focuses on the applications of intelligent methods in modeling and optimization of renewable energy systems. In this regard, original papers with novel ideas on the modeling and optimization of different renewable energy systems are welcome to be submitted to this issue. In addition to original articles, high quality technical notes and review papers will be considered for possible publication. The main subjects -but not limited to- of this issue are as follows:
• Utilization of data-driven methods in modeling renewable energy systems,
• Optimization of renewable energy technologies by applying intelligent algorithms,
• Modeling the properties of the materials used in renewable energy technologies by applying data driven methods,
• Developing novel approaches for modeling and optimization of renewable energy systems.
Keywords: Renewable Energy Systems, Optimization, Modeling, Intelligent Methods, Artificial Neural Network
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.
