This article presents a variety of different approaches to both model and assess the performance of daylight-integrated electric lighting control systems. In these systems, the output of a controlled lighting zone is based on a light sensor reading and a calibrated control algorithm. Computer simulations can consider the simulated illuminance data generated from both the electric lighting system and a daylight delivery system whose performance is addressed using typical meteorological year (TMY) weather data. Photosensor signals and the operation of a control system’s dimming algorithms are also included. Methods and metrics for evaluating simulated performance for the purpose of making informed design decisions that lead to the best possible installed system performance are presented.

Longwave radiative heat transfer is a key determinant of energy consumption in buildings and view factor calculations are therefore required for the detailed simulation of heat transfer between buildings and their environment as well as for heat exchange within rooms. Typically, these calculations are either derived through analytical means or performed as a part of the simulation process. This paper describes the methodology for employing RADIANCE, a command-line open-source raytracing software, for performing view factor calculations. Since it was introduced in the late-1980s, RADIANCE has been almost exclusively employed as the back-end engine for lighting simulations. We discuss the theoretical basis for calculating view factors through Monte Carlo calculations with RADIANCE and propose a corresponding workflow. The results generated through RADIANCE are validated by comparing them with analytical solutions. The fundamental methodology proposed in this paper can be scaled up to calculate view factors for more complex, practical scenarios. Furthermore, the portability, multi-processing functionality and cross-platform compatibility offered by RADIANCE can also be employed in the calculation of view factors.

In the last two decades, numerous studies have demonstrated the viability of using High Dynamic Range Imaging (HDRI) to quantify lighting conditions in the built environment. Several human factor studies have demonstrated correlation between visual comfort perceived by occupants and glare metrics calculated by analysing HDR images. However, the use of HDRI in real-world applications has been severely limited owing to privacy concerns. This research investigates the feasibility of employing obfuscated (i.e. deliberately distorted) HDR images for analysing glare. The authors present a pilot study where visual conditions inside an office-space were simulated and captured as HDR images using a validated, physically-based renderer. The images were then obfuscated to various degrees by application of blur filters. Glare metrics calculated for the obfuscated images, when compared with the metrics generated for the original HDR images, were found to be within 2%-12% relative error. The proof-of-concept demonstrated through this study provides the framework for field-testing of an HDR-based lighting control system in a real office space.

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