Facades, as a building component, directly impact indoor comfort conditions. Different facades systems have been developed not only to cope with thermal and visual comfort aspects, but also for energy efficiency, use of daylight, and natural ventilation. Designers consider Double Skin Facade (DSF) systems in educational buildings to improve the abovementioned performance aspects. Recent works deal with a fixed room depth for proposed parametric DSF models, considering fundamental versions of well-known optimisation algorithms. Since the educational buildings require various room depths based on the function of the building, optimum daylight performance may not be achieved with the same cavity gap utilizing only one well-known solver. This study proposes a computational framework to cope with this problem in DSFs for educational buildings. In this context, the study suggests a parametric educational space with a DSF system and optimised seven design parameters, including twelve room depth scenarios by using three single objective optimisation algorithms. The study suggests optimised design scenarios for spatial Daylight Autonomy (sDA) and Annual Sun light Exposure (ASE) in the region between 35(o)-40(o) N latitudes of the Mediterranean CSA climate type(1). Although previous works have frequently used the genetic algorithm (GA) to solve DSF design problems, results showed that GA is unsuitable for coping with sDA and ASE. The results indicated that a single opening is insufficient to provide optimum sDA and ASE in educational buildings with DSF systems, which room depth of more than 7 m.
Ekici et al. (Thu,) studied this question.