An Evolutionary Computing Approach For Simultaneous Daylight Optimization in Urban Environments and Buildings Interiors

Abstract

Rapid population growth globally is resulting in urban densification exponentially. As cities become denser, the environmental quality of urban canyons reduces, resulting in an increase in associated energy use in buildings. Currently, cities are responsible for 70% of the world’s energy consumption. One of the efficient solutions to address this issue is allowing more solar access into interiors and thus making the most of daylight and solar heat gain. Accordingly, this paper presents a novel approach to integrate daylight optimization in both urban environments and buildings’ interiors via the development and application of a custom algorithm based evolutionary computation. This ultimately allows more daylight penetration into urban canyons [vertical daylight illuminance (VDI)] and, subsequently, improves indoor visual comfort [useful horizontal illuminance level (HIL)]. This can also reduce the associated lighting and heating (during winter) energy use of buildings. Furthermore, investigating the correlation between indoor and outdoor illuminance levels aims to bridge the gap between daylight requirements at the urban planning and building scale. A multi-objective evolutionary algorithm-based assessment using computational simulation of design variables is conducted. This determines the extent to which each urban morphology can affect daylight access in both indoor and outdoor environments. Accordingly, the optimal range for different design factors is suggested.