Design optimization of daylighting for kindergarten in different light climate zones in China

Kindergarten classrooms in China undertake the functions of teaching, activities, breaks and diet, so daylighting design is particularly important. This paper proposed using artificial neural network (ANN) model to replace the typical physical model to optimize the daylighting quality and daylighting quality uniformity in the classroom, in order to achieve efficient optimization calculation. In detail, the problems in the classroom daylighting design are firstly investigated, and a daylighting design structure that incorporates the south window and north skylight of a kindergarten classroom is proposed. Then, the evaluation indicators of daylighting quality and daylighting quality uniformity are determined. Finally, the ANN model is used to achieve efficient optimization calculation. The optimization effect of kindergarten classrooms in five light climate zones in China is discussed. The results show that compared with the benchmark scheme in the existing design codes, after single objective optimization, in the kindergarten classroom can be increased by at most 18.2%, while UDIav can be increased by at most 50.9%. After double-objective optimization, DA(av )can be increased by at most 17.7%, and can be decreased by at most 87.7%; can be increased by at most 50.5%, and UDISD can be decreased by at most 97.6%.