AI-Driven Thermal Systems Optimization
Abstract
Thermal systems are integral to a wide range of industrial and domestic
applications, including heating, ventilation, air conditioning (HVAC),
refrigeration, and energy generation. The complexity of these systems arises from the interaction of multiple components, dynamic environmental conditions, and energy efficiency constraints. Artificial intelligence (AI) techniques, such as machine learning (ML), neural networks, and genetic algorithms, offer significant potential to optimize thermal system performance in real-time. This paper reviews recent advancements in AI-driven thermal systems optimization, highlighting methods, applications, and challenges. It also presents comparative analyses of AI techniques in different thermal systems and suggests directions for future research. The review emphasizes the potential of AI to reduce energy consumption, improve system reliability, and
enhance sustainability.
KEYWORDS: Thermal systems, AI optimization, machine learning, energy
efficiency, HVAC, predictive control
applications, including heating, ventilation, air conditioning (HVAC),
refrigeration, and energy generation. The complexity of these systems arises from the interaction of multiple components, dynamic environmental conditions, and energy efficiency constraints. Artificial intelligence (AI) techniques, such as machine learning (ML), neural networks, and genetic algorithms, offer significant potential to optimize thermal system performance in real-time. This paper reviews recent advancements in AI-driven thermal systems optimization, highlighting methods, applications, and challenges. It also presents comparative analyses of AI techniques in different thermal systems and suggests directions for future research. The review emphasizes the potential of AI to reduce energy consumption, improve system reliability, and
enhance sustainability.
KEYWORDS: Thermal systems, AI optimization, machine learning, energy
efficiency, HVAC, predictive control