Evaluating thermal comfort in residential buildings using double-skin facades with particle swarm optimization

Abstract

Ensuring optimal thermal comfort while maintaining energy efficiency is a crucial challenge in residential building design. Double-Skin Facades (DSFs) have emerged as an innovative architectural solution, providing passive temperature regulation through controlled ventilation and insulation. However, optimizing DSF parameters, such as cavity depth, window type, and airflow mechanisms, requires advanced computational techniques. This study employs Particle Swarm Optimization (PSO) to enhance the effectiveness of DSFs in improving thermal comfort and reducing energy consumption. A simulation-based approach is used, integrating EnergyPlus for thermal performance analysis and GenOpt for optimization. The study focuses on optimizing Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD)—two widely accepted thermal comfort indices based on ASHRAE 55 and ISO 7730 standards. Through PSO-driven adjustments, DSF configurations are refined to maintain indoor environmental stability while minimizing cooling and heating loads. The results demonstrate that optimized DSFs significantly enhance occupant comfort and lead to substantial energy savings, particularly in extreme climate conditions. The study also presents a comparative analysis of pre- and post-optimization energy consumption, showing reductions in both heating and cooling demands. PSO convergence graphs and energy savings visualizations highlight the effectiveness of the optimization framework. These findings contribute to sustainable building design by providing a computational methodology that enables architects and engineers to develop high-performance residential buildings. Future research could integrate machine learning techniques to further refine real-time adaptive control mechanisms for DSF configurations.