Hybrid AI-Driven Multi-Objective Optimization of HVAC Systems for Energy Efficiency and Thermal Comfort

HVAC systems account for a substantial share of building energy use and are central to maintaining indoor comfort and sustainability, making their optimization a critical research focus. However, improving HVAC performance involves balancing conflicting objectives such as reducing energy consumption and operational cost while ensuring thermal comfort and acceptable indoor air quality. To address this complexity, this study conducts a systematic review using structured methodologies, including PRISMA-based screening, bibliometric evaluation, and taxonomy-driven classification of existing approaches. The analysis reveals a clear progression in optimization techniques, moving from traditional deterministic methods to advanced artificial intelligence and hybrid models that combine learning and optimization capabilities. These developments have significantly enhanced the ability to manage dynamic and multi-objective HVAC systems. The study further provides a comprehensive comparative framework to evaluate different techniques and highlights key research gaps. Finally, it outlines future directions aimed at developing more adaptive, efficient, and intelligent HVAC optimization solutions.