Based on a new approach for the prediction of the Daylight Factor (DF), using existing empirical models, this research work presents an optimization of window size and daylight provided by the glazed apertures component for a building located in a hot and dry climate. The new approach aims to improve the DF model, considering new parameters for daylight prediction such as the orientation, sky conditions, daytime, and the geographic location of the building to fill in all the missing points that the standard DF, defined for an overcast sky, presents. The enhanced DF model is considered for the optimization of window size based on Non dominated Sorting Genetic Algorithm (NSGA II), for heating and cooling season, taking into account the impact of glazing type, space reflectance and artificial lighting installation. Results of heating and cooling demand are compared to a recommended building model for hot and dry climate with 10% Window to Wall Ratio (WWR) for single glazing. The optimal building model is then validated using a dynamic convective heat transfer simulation. As a result, a reduction of 48% in energy demand and 21.5% in CO2 emissions can be achieved. The present approach provides architects and engineers with a more accurate daylight prediction model considering the effect of several parameters simultaneously. The new proposed approach, via the improved DF model, gives an optimal solution for window design to minimize building energy demand while improving the indoor comfort parameters.

This study aims to show the impact of orientation and glazing type on optimum window size in hot climate using genetic algorithms. In winter the optimization of window size is obtained thanks to thermal gains from solar radiation, taking into account this free heat gains from the sun reduces heating demand of the building. In summer the optimization of window size is complex, in this case, the window is considered as a heat gains element. For a hot climate, the sun can be used as a passive strategy to reduce energy consumption. An optimal window size allows avoiding problems of glare and overheating. ASHRAE proposed a Window to Wall Ratio (WWR) which is considered as the optimal window size that ensures minimum annual thermal loads, this coefficient neglect different parameters such as (Glazing type, the orientation, daytime). A typical office room located in Ghardaia (South of Algeria) is selected as a case study. The results show that daylight is a key factor in limiting the window size in hot climate. The WWR cannot be considered as optimal for the whole year; this study shows that the optimal window size varies with daytime.