AI-Driven Optimization And Strategic Use Of Phase Change Materials For Smart Thermal Regulation

Abstract

Artificial Intelligence (AI) has been introduced as a transformative solution to increasing energy efficiency in residential and industrial buildings, industrial systems, and intelligent devices with the usage of advanced thermal management technologies. The temperature regulating characteristics of Phase Change Materials (PCMs) have been known since long and it is therefore considered by the experts to store heat and release latent heat which may stabilise fluctuations in temperature and limit spiking of energy. Nevertheless, they have problems in their practical implementation associated with the choice of the most effective materials, methods of their arrangement, and control in conditions of dynamic changes of working and environmental conditions. The current work addresses an AI-based optimization strategy that combines tactical applications of PCMs in smart thermal management. The study integrates models of computation, simulations of building energy, and machine learning methods to determine the most viable PCM architectures based on climatic regions and specifications of operation. The experimental verification testbed is a field testbed (technology hybrid laboratory) which verifies the real-time assessment of thermal response, energy savings and occupant comfort. Evidence shows that the incorporation of AI-based PCM has the potential to provide 28-35% increase in thermal stability and 20-25% drop in cooling/heating energy requirements than the traditional modes of PCM implementation. The given methodology does not only optimize the use of materials and lower the operational expenditure but also establishes the future adoption of adaptive and self-learning thermal control systems in smart infrastructures of the future.