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This study evaluates the performance and robustness of 22 established and newly proposed glare prediction metrics. Experimental datasets of daylight-dominated workplaces in office-like test rooms were collected from studies by seven research groups in six different locations (Argentina, Denmark, Germany, Israel, Japan and the United States). The variability in experimental setups, locations and research teams allowed reliable evaluation of the performance and robustness of glare metrics for daylight-dominated workplaces. Independent statistical methods were applied to individual datasets and also to one combined dataset to evaluate the performance and robustness of the 22 glare metrics. As performance and robustness are not established in literature, we defined performance as: (1) the ability of the metric value to describe the glare scale (evaluated by Spearman rank correlation), and (2) the ability of the metric to distinguish between disturbing and non-disturbing situations (evaluated by diagnostic receiver operating characteristic curve analysis tests). Furthermore, we defined robustness as the ability of a metric to deliver meaningful results when applied to different datasets and to fail as few as possible statistical tests. Average Spearman rank correlation coefficients in the range of 0.55–0.60 as well as average prediction rates to distinguish between disturbing and non-disturbing glare of 70–75% for several of the metrics indicate their reliability. The results also show that metrics considering the saturation effect as a main input in their equation perform better and are more robust in daylight-dominated workplaces than purely contrast-based metrics or purely empirical metrics. In this study, the daylight glare probability (DGP) delivered the highest performance amongst the tested metrics and was also found to be the most robust. Future research should aim to optimise the terms of glare equations which combine contrast and saturation effects, such as DGP, PGSV or UGRexp, to achieve metrics that also perform reliably in dimmer lighting conditions than the ones explored in this study.

In the field of lighting, luminance maps are often used to evaluate point-in-time lighting scenes from the occupant's vantage point. High Dynamic Range (HDR) photography can be used to generate such luminance maps. The aim of this tutorial is to present a comprehensive overview of a step-by-step procedure to generate a 180°luminance map of a daylit scene from a sequence of multiple exposures with semiprofessional equipment and the Radiance suite of programs. The procedure consists in capturing a sequence of multiple exposures of the visual scene; selecting the useful exposures; merging the exposures to generate the HDR image by using the predefined camera response function; nullifying the exposure value; resizing and cropping the HDR image by using the predefined fisheye view coordinates; adjusting the projection of the HDR image by using the predefined distortion function; correcting the vignetting of the HDR image by using the predefined vignetting curves; correcting the alterations of the HDR image due to the Neutral Density (ND) filter if one was used, by using the predefined ND correction function; adjusting the photometry of the HDR image by using the measured spot luminance value; editing the HDR image header by using the predefined projection type and real viewing angle; and checking the validity of the HDR image by using the measured vertical illuminance, and, if needed, the predefined luminous range. To conclude, an analysis of errors is made and attention points to adapt the procedure for electric or circadian lighting studies are discussed.

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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.

Exposure to daylight has much to offer and should be optimised to maximise its potential. In order to harvest its benefits, any visual discomfort from daylight should be anticipated and minimised. Hence, there is the need to predict discomfort from daylight glare. While more than 20 models for predicting discomfort from daylight glare have been developed, none accurately predict it. The inclusion of additional factors in the models may improve the predictions. One such factor is the socio-environmental context of the observer. This study compares the evaluations of discomfort glare from daylight for office buildings in four socio-environmental contexts: Chile, Belgium, Japan and Switzerland. The evaluations of discomfort glare, each consisting of subjective assessments and physical measurements of a view condition, were collected at the office desks of 401 participants, although only 211 responses were used in the analyses due to exclusion rules. The results do not suggest evidence of an influence of socio-environmental context on discomfort from daylight glare. In other words, the participants in this study perceived discomfort glare similarly, regardless of their socio-environmental context.

For the application of discomfort glare metrics, a categorisation is used, dividing the metric scale into categories of perception. These categories are separated by borderline values, or so-called cut-off values. Recent literature shows that these cut-off values are lower when they are derived from field study data than those derived from laboratory study data. To investigate this further, the data from one field study and two laboratory studies was used to derive and compare cut-off values corresponding to three borderlines. The results show that the field study cut-off values were systematically lower than the laboratory study ones, implying that discomfort glare is reported at lower stimulus magnitudes in the field. Although further research is required on that topic, several hypotheses are discussed in order to explain the gap between cut-off values derived from field data and cut-off values derived from laboratory data. Recommendations for future studies are also provided.

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