Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108225
Title: Special issue on artificial intelligence in thermal engineering systems
Authors: Xiao, F 
Guo, F 
Fan, C
Besagni, G
Issue Date: 5-Jan-2024
Source: Applied thermal engineering, 5 Jan. 2024, v. 236, 121894
Abstract: The special issue “AI in Thermal Engineering” covers the most recent studies with a focus on the applications of artificial intelligence (AI) technologies in thermal engineering systems. The overall aim is to report the latest advances of research and development, discuss the pros and cons, and explore the future perspectives on the synergy of AI and thermal engineering. Articles reporting original research contributions and critical reviews on adopting AI to address engineering problems of modeling, prediction, control, optimization, performance assessment, diagnosis of thermal engineering devices, components and systems are welcome. Special focus is given to those problems which have not been adequately addressed by adopting traditional methods due to limited knowledge and information, computation efficiency, capability of generalization and adaptation in applications, etc. The targeted engineering systems include energy storage devices, power plants, heat pumps and cooling or refrigeration plants, combined heat and power plants, buildings and district energy systems, renewable and clean energy systems, and other engineering systems involving thermal engineering processes. The special issue has received over 30 submissions, and a total of 10 technical papers are selected for publication which cover a broad range of applications including building energy systems, thermal power units, vehicles, and heat transfer processes. These papers addressed challenges and research gaps in adopting AI in real applications, such as model development and adaption, model interpretability, data imbalance, missing data imputation, etc.
Publisher: Elsevier Ltd
Journal: Applied thermal engineering 
ISSN: 1359-4311
EISSN: 1873-5606
DOI: 10.1016/j.applthermaleng.2023.121894
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