Artificial intelligence and thermal energy storage: A review of design techniques and applications

Thermal energy storage systems (TES) are becoming increasingly popular owing to its great energy capacity and efficiency. However, traditional TES design methods are often time-consuming, and due to the intricate and nonlinear nature of such systems, the results could be unreliable. The design and improvement of TES has made use of artificial intelligence (AI) techniques such artificial neural networks, fuzzy logic, evolutionary algorithms, and machine learning models. This review paper offered a thorough analysis of AI techniques for TES system design and optimization, comparing them to conventional design approaches, outlining the benefits and drawbacks of AI optimization techniques, and discussing how AI was used in TES systems. The purpose of the study was to define the existing issues, opportunities, and prospects for research in this field, as well as to provide advice for researchers, practitioners, and policymakers about the selection and deployment of AI approaches for TES system design. The article showed how AI techniques had the potential to improve the functionality, effectiveness, and long-term viability of TES systems by looking at recent research and cases. • AI techniques can optimize TES system design parameters to maximize performance. • Prediction with AI can be more accurately than with traditional methods. • AI optimization methods can reduce time and computational costs of TES design. • There is a need for further research comparing different AI methods for TES system.