Optimizing Thermal Load in Compact Buildings: A Comparative Analysis of Single and Hybrid Metaheuristics for Balanced HVAC Efficiency

Usman Usman (Department of Engineering Science, Faculty of Engineering, Yogyakarta State University, Yogyakarta, Indonesia)
Fatchul Arifin (Department of Electronics and Informatics Engineering Education, Yogyakarta State University, Yogyakarta, Indonesia)
Rustam Asnawi (Department of Electrical Engineering Education, Yogyakarta State University, Yogyakarta, Indonesia)

Journal of Applied Engineering and Technological Science (JAETS)
Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)

This work is a comprehensive comparison study of five metaheuristic algorithms — namely GA, PSO, SA, GA-PSO, and GA-PSO-SA — using 3,000 model evaluations across 30 independent runs to optimize compact building thermal loads. Statistical analysis demonstrates that the SA, GA-PSO, and GA-PSO-SA present similar accuracy (RMSE: 3.55±0.16 kWh/m²) without any statistical difference (p> 0.97), contradicting the hypothesis that complexity in hybrid promotes the actual performance. GA appears to be the best compromise, with the highest efficiency ratio (11.47), providing 97.5% of SA's accuracy at 42% lower computational cost. The instability shown by PSO is quite alarming (CV: 14.215%, performance spread: 53.6%) and clearly indicates premature convergence, which sharply contradicts its claim of outperforming in continuous optimization [1]. Sensitivity analysis results show that envelope thermal properties, particularly the wall U-value (NSC=1.43), have 7.5× more influence on prediction performance than building orientation, providing evidence supporting the argument that input data quality outweighs algorithm choice for HVAC design-type applications.