  In Chinese building codes, it is required that residential buildings receive
a minimum number of hours of natural, direct sunlight on a specified winter
day, which represents the worst sunlight condition in a year. This requirement
is a prerequisite for obtaining a building permit during the conceptual design
of a residential project. Thus, officially sanctioned software is usually used
to assess the sunlight performance of buildings. These software programs
predict sunlight hours based on repeated shading calculations, which is
time-consuming. This paper proposed a multilayer perceptron-based method, a
one-stage prediction approach, which outputs a shading time interval caused by
the inputted cuboid-form building. The sunlight hours of a site can be obtained
by calculating the union of the sunlight time intervals (complement of shading
time interval) of all the buildings. Three numerical experiments, i.e.,
horizontal level and slope analysis, and simulation-based optimization are
carried out; the results show that the method reduces the computation time to
1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning
plug-in for Rhino 7/Grasshopper is also developed based on the proposed model.
This paper indicates that deep learning techniques can be adopted to accelerate
sunlight hour simulations at the conceptual design phase.
