Artificial Intelligence‐Driven Energy Optimization in Heating, Ventilation, and Air Conditioning Systems

The growing demand for energy-efficient thermal management in industrial and commercial systems has spurred innovations in fluid-based cooling and liquid flow optimization. Traditional design and control methods rely on fixed models and heuristics, which limit adaptability to changing conditions. This study introduces a robust framework, combining game theory with partial differential equations (GTPDE) and advanced machine learning techniques to enhance the energy efficiency and operational performance of heating, ventilation, and air conditioning (HVAC) systems, major contributors to energy consumption in industrial environments. In the first phase, game theory principles model fluid flow dynamics by treating each pump as a rational decision-maker. Payoff matrices simulate operational strategies under varying conditions, enabling the identification of optimal parameter combinations to maximize energy efficiency and reduce costs. The second phase employs supervised learning and deep reinforcement learning (DRL) algorithms to predict fluid flow rates, with a hybrid optimum support vector machine (SVM) and DRL model showing superior accuracy and control over fluid dynamics. Finally, the third phase integrates game theory with the optimum SVM model for predictive maintenance, minimizing equipment downtime and maintenance costs while ensuring reliable system operation. This framework offers practical benefits, including optimized energy use, cost savings, enhanced reliability, and predictive maintenance for HVAC systems, making it a valuable contribution to energy-efficient industrial practices.