THERMALSENSE: AI-DRIVEN MODELING AND OPTIMIZATION OF ENERGY SYSTEMS

Abstract

Thermal systems play a vital role in industrial, power generation, refrigeration, and energy conversion applications. Improving their performance and efficiency is essential for reducing energy consumption and environmental impact. Conventional modeling and optimization approaches often struggle with the nonlinear, complex, and multi-parameter nature of thermal systems. This paper presents an intelligent modeling and optimization framework for thermal systems using computational intelligence techniques. Artificial intelligencebased models are employed to accurately represent system behavior, while optimization algorithms are applied to enhance thermal performance under varying operating conditions. The proposed approach enables effective handling of nonlinearities, uncertainty, and complex interactions among system parameters. Experimental evaluation demonstrates significant improvement in efficiency, heat transfer rate, and energy utilization compared to traditional methods. The results confirm that computational intelligence provides a powerful and flexible tool for performance enhancement of modern thermal systems