Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Designs

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