Machine learning methods, such as artificial neural networks and support vector machines, are widely used in different fields of science due to their efficient performance in predicting the behavior of the systems. Performance, accuracy and reliability of the machine learning methods are dependent on various factors such as applied algorithm, considered input, complexity of the problem and etc. In recent years, machine learning approaches have attracted the attention of the researchers working in the field of thermal energy systems. These approaches are applicable in different aspects of thermal engineering such as modeling of thermophysical properties, outputs of heat transfer devices, predicting the performance of thermal power plants, etc. In addition to modeling and predicting the behavior of the thermal systems, by using these methods the importance of the inputs can be determined which facilitate upgrading and modification of thermal systems. This Research Topic mainly focuses on the applications of machine learning approaches in modeling and predicting the performance of thermal systems. High quality Original Research and Review studies are invited to be submitted. The most interesting areas of the research for the present collection are as follows:
• Predicting the output and efficiency of thermal systems by using different machine learning methods;
• Investigation of the factors influencing the performance of machine learning methods used for modeling thermal systems;
• Applications of novel artificial intelligence methods in thermal engineering;
• Applications of machine learning approaches in energetic and exergetic modeling of thermal power plants;
• Comparison of different machine learning approaches, in terms of computational cost, accuracy and speed, used for thermal systems modeling;
• Applications of machine learning approaches in modeling thermophysical properties of materials;
• Applications of machine learning methods in the control systems applied in thermal systems.
In addition to the above-mentioned topics, articles with high quality in relevant fields will be considered for publication.
Machine learning methods, such as artificial neural networks and support vector machines, are widely used in different fields of science due to their efficient performance in predicting the behavior of the systems. Performance, accuracy and reliability of the machine learning methods are dependent on various factors such as applied algorithm, considered input, complexity of the problem and etc. In recent years, machine learning approaches have attracted the attention of the researchers working in the field of thermal energy systems. These approaches are applicable in different aspects of thermal engineering such as modeling of thermophysical properties, outputs of heat transfer devices, predicting the performance of thermal power plants, etc. In addition to modeling and predicting the behavior of the thermal systems, by using these methods the importance of the inputs can be determined which facilitate upgrading and modification of thermal systems. This Research Topic mainly focuses on the applications of machine learning approaches in modeling and predicting the performance of thermal systems. High quality Original Research and Review studies are invited to be submitted. The most interesting areas of the research for the present collection are as follows:
• Predicting the output and efficiency of thermal systems by using different machine learning methods;
• Investigation of the factors influencing the performance of machine learning methods used for modeling thermal systems;
• Applications of novel artificial intelligence methods in thermal engineering;
• Applications of machine learning approaches in energetic and exergetic modeling of thermal power plants;
• Comparison of different machine learning approaches, in terms of computational cost, accuracy and speed, used for thermal systems modeling;
• Applications of machine learning approaches in modeling thermophysical properties of materials;
• Applications of machine learning methods in the control systems applied in thermal systems.
In addition to the above-mentioned topics, articles with high quality in relevant fields will be considered for publication.
