Radiance matrix-based methods enable efficient parametric simulations, allowing users to vary sky conditions, fenestration systems, and other model parameters at a minimal cost to computation. However, the steep learning curve and complex workflow hinder the widespread adoption of matrix-based methods. The frads Python library with a series of command-line tools was developed to automate the entire matrix-based simulation process, lowering entry barriers and reducing human error. Co-simulation between EnergyPlus and Radiance was also enabled using the Python library from EnergyPlus.

Bidirectional scattering distribution function (BSDF) data are used in design practice to represent optically complex daylighting and solar control systems in lighting and energy simulation software. Visual comfort assessments (e.g., daylight glare) require accurate determination of luminance and corresponding solid angle of glare sources. For the sun, the necessary resolution of the BSDF causes problems both in terms of data volume and computational effort. With “peak extraction” (PE), we present a new method that simulates the direct solar contribution at its real size and spread, while efficiently using the underlying BSDF data set for the scattered light. PE enables practitioners to evaluate daylight performance metrics for their designs at improved accuracy.