Data-Driven Optimization of Traditional Korean Hanok Ventilation Strategies for Smart Building Energy Efficiency

**Abstract:** This paper investigates the application of advanced data analytics and machine learning techniques to optimize ventilation strategies derived from traditional Korean Hanok architecture (specifically showcasing *Cheoma*, *Daechung*, and *Ondol* principles) for modern smart building design. We present a framework leveraging detailed building performance simulation data and reinforcement learning to dynamically adjust ventilation parameters, minimizing energy consumption while maintaining occupant comfort and air quality. This proposed approach addresses limitations of static energy models and offers a potentially 15-20% improvement in energy-efficient ventilation compared to conventional HVAC systems in comparable climates, leading to reduced carbon footprint and operational costs. The system relies on rigorously validated physics-based simulations enabled by detailed 3D modeling and takes into account dynamic weather patterns.