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Annual daylighting and point-in-time glare calculations depend on manipulating large matrices representing annual light levels and image pixels, respectively. Advances in computation speed have allowed modern green building standards like LEED, WELL, and EN 17037 to require annual daylighting calculations at a fine resolution across a building's floorplate. As parallel ray tracing speeds become faster and cameras increase in resolution, matrix manipulation itself has emerged as a bottleneck to delivering fast simulation results. This paper describes a how vectorized computation methods can be applied to annual daylighting simulation and high dynamic range (HDR) image processing. Vectorization allows a computer to perform simple operations on large amounts of data simultaneously. We use the NumPy library in Python to vectorize multiple steps in the calculation of spatial daylight autonomy and annual renderings for glare as example climate-based daylighting metrics, achieving a speedup of two orders of magnitude. We also show that NumPy can manipulate large HDR images with speedups for individual operations up to six orders of magnitude. [ABSTRACT FROM AUTHOR]

Building performance simulations and models of human visual comfort allow us to predict daylight-caused glare using digital building models and climate data. Unfortunately, the simulation tools currently available cannot produce results fast enough for interactive use during design ideation. We developed software with the ability to predict visual discomfort in real time. However, we know little about how users react to simulation feedback presented in real time. In our study, 40 subjects with backgrounds in building design and technology completed two shading design exercises to balance glare reduction and annual daylight availability in two open office arrangements using two simulation tools with differing system response times. Subjects with access to real-time simulation feedback tested more design options, reported higher confidence in design performance and increased satisfaction with the design task, and produced better-performing final designs with respect to spatial daylight autonomy and enhanced simplified daylight glare probability. [ABSTRACT FROM AUTHOR]

Image-based visual discomfort analysis has strong potential to detect glare in order to predict occupant satisfaction with a space. However, in order to design buildings with occupant visual comfort in mind, architects need the ability to predict glare through renderings, rather than photographs. In this paper, we compare glare measurements from high dynamic range photographs and computer renderings of the same daylit space under 240 clear and 38 cloudy sky conditions in order to assess how accurately architects can predict glare though simulation. We use two ray tracing engines, R adiance and Accelerad, on scenes illuminated by a mathematically defined sky model or by recorded outdoor luminance levels from photographs. Depending on the scene, these methods predict daylight glare probability levels due to bright sources with between 93% and 99% accuracy and discomfort glare due to contrast with between 71% and 99% accuracy. Glare predictions are more sensitive to modeling and measurement accuracy than to simulation parameters, so accurate results can come from relatively fast simulations. Furthermore, with graphics acceleration, we are able to produce accurate glare predictions within minutes using Accelerad, achieving a speedup over R adiance of between 16 and 44 times. [ABSTRACT FROM AUTHOR]