Daylight Discomfort Glare Evaluation with Evalglare: Influence of Parameters and Methods on the Accuracy of Discomfort Glare Prediction

Magali Bodart,Clotilde Pierson,Jan Wienold

doi:10.3390/buildings8080094

Magali Bodart, Clotilde Pierson + Show 1 more

Open Access

https://doi.org/10.3390/buildings8080094

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Abstract

Nowadays, discomfort glare indices are frequently calculated by using evalglare. Due to the lack of knowledge on the implications of the methods and parameters of evalglare, the default settings are often used. But wrong parameter settings can lead to inappropriate glare source detection and therefore to invalid glare indices calculations and erroneous glare classifications. For that reason, this study aims to assess the influence of several glare source detection methods and parameters on the accuracy of discomfort glare prediction for daylight. This analysis uses two datasets, representative of the two types of discomfort glare: saturation and contrast glare. By computing three different statistical indicators to describe the accuracy of discomfort glare prediction, 63 different settings are compared. The results suggest that the choice of an evalglare method should be done when considering the type of glare that is most likely to occur in the visual scene: the task area method should be preferred for contrast glare scenes, and the threshold method for saturation glare scenes. The parameters that should be favored or avoided are also discussed, although a deeper understanding of the discomfort glare mechanism and a clear definition of a glare source would be necessary to reliably interpret these results.

Highlights

According to the International Commission on Illumination (CIE), glare is defined as the “condition of vision in which there is discomfort [discomfort glare] or a reduction in the ability to see significant objects [disability glare], or both, due to an unsuitable distribution or range of luminances or to extreme contrasts in space or time” [1]
The first graph uses the results of the first statistical approach, namely the Spearman correlation; the second graph uses the results of the second statistical approach, namely the AUC of the binary logistic regression models; and, the third graph uses the results of the third statistical approach, namely the AICc of the ordinal logistic regression models
The statistical results—Spearman correlation and logistic regressions—that were found to be non-significant with Bonferroni correction are still included in the graphs, but they can be recognized by graphical means

Summary

Introduction

According to the International Commission on Illumination (CIE), glare is defined as the “condition of vision in which there is discomfort [discomfort glare] or a reduction in the ability to see significant objects [disability glare], or both, due to an unsuitable distribution or range of luminances or to extreme contrasts in space or time” [1]. There are four main physical quantities, on which discomfort glare indices are based [1]: the luminance of the glare source(s) in the field of view, the solid angle of the glare source(s), the luminance of the background, and the position index of the glare source(s). To measure these quantities efficiently in situ, High Dynamic Range (HDR) imaging technique is used to create a 180◦ luminance map containing the subject’s field of view. Discomfort glare indices are calculated afterward on the basis of these luminance maps. Luminance Definition study, the task area method would be more appropriate since the adaptation level of the subject’s

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