Completed PhD Thesis at LIPID

Discomfort glare prediction metrics are used in many contexts, from daylight simulations and shading control to building recommendations and standards. However, the performance of such metrics may be limited to the dataset from which they were developed which, as is the case for Daylight Glare Probability (DGP), typically relied on laboratory user studies conducted in brightly lit conditions. This thesis investigates the performance of discomfort glare metrics in dim daylit office scenarios, with the goal of broadening the applicability of existing prediction metrics to lower brightness ranges. It was indeed found that, while hybrid metrics such as DGP, i.e. metrics combining a saturation term (for brightness) with a contrast term, outperform saturation-driven metrics in general, contrast-driven metrics are most reliable in dimmer scenes with lower adaptation levels. To supplement existing laboratory datasets, two user studies on contrast-dominant discomfort glare from daylight were conducted and ultimately led to recommendations for modifying the contrast term in DGP to accommodate a range of contrast effects of glare in daylit scenarios relevant to office environments.

Accepted without reservation, the thesis will be presented to the public on November 28th, 2022.