Response surface model-based multi-objective optimization of Box–Behnken building design for indoor comfort and energy

Rapid urbanization and the increasing energy demand of buildings necessitate modern building optimization techniques to maintain a suitable equilibrium between indoor comfort and building energy demand. This investigation aims to maintain an optimal balance between the building energy demand and indoor occupant comfort. Efforts have been made to optimize the independent variables influencing Indoor Environmental Quality (IEQ) response variables through a combination of advanced statistical and numerical techniques including the Box–Behnken design, response surface methodology (RSM), analysis of variance (ANOVA) and computational fluid dynamics (CFD). The statistically optimized solution obtained using RSM determined the optimal parameters: inlet diffuser inclination (IDI) = −76.52, inlet air velocity = 0.1 m/s, and radiator temperature = 300 K. The statistical model demonstrated strong reliability, with a mean root mean square error (RMSE) of 4.73% compared to numerical results with an R ² of .998. The IDI appeared as the primary factor influencing building performance, with significant quadratic effects on operative temperature, indoor air velocity, and the predicted mean vote (PMV) index, highlighting precise modeling for indoor comfort assessment. The statistical methods applied in this study proved highly effective for numerical and experimental analyses. They hold significant potential for modeling indoor environments while addressing ventilation, air quality, thermal comfort, and energy efficiency.