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Solar shading can be an effective way of avoiding overheating by reducing solar gains in buildings. However, solar shading systems can block or obscure the view-out with the consequence that occupants may refrain from using the shading system. As such, there is a need to quantify the effects of shading on occupants’ perception of the view. In this study, we developed a method to identify the photometric parameters and compositions that are effective in characterising the view-out in relation to different solar-control systems. We hypothesized that the photometric composition, e.g., contrast, in the visual environment as a result of using solar-control systems impacts the subjective assessment of the view. We conducted objective photometric measurements using calibrated luminance cameras and subjective responses from 64 participants in a semi-controlled work environment to test the hypotheses. The participants were randomly allocated to a combination of five view-outs and six solar shading systems in a work environment where they answered questions related to the indoor environment and view quality. The relation between view and solar-control systems and their impact on the subjective view assessment was tested using linear mixed-effects models. The models were developed using forward and backward selection based on AIC and likelihood ratio tests (LRT) to test the effects of adding or removing variables. The achromatic contrast calculated based on the measured luminance data both locally and globally was significantly associated with view assessment and satisfaction when using different shading types.

The perception, physiology, behavior, and performance of building occupants are influenced by multi-domain exposures: the simultaneous presence of multiple environmental stimuli, i.e., visual, thermal, acoustic, and air quality. Despite being extensive, the literature on multi-domain exposures presents heterogeneous methodo-logical approaches and inconsistent study reporting, which hinder direct comparison between studies and meta -analyses. Therefore, in addition to carrying out more multi-domain studies, such investigations need to be designed, conducted, and documented in a systematic and transparent way. With the goal to facilitate and support future multi-domain studies and their meta-analyses, this work provides (1) a range of criteria for multi -domain study design and reporting (i.e., defined as quality criteria), and (2) a critical review of the multi-domain literature based on the described criteria, which can serve as guidelines and recommendations for future studies on the topic. The identified quality criteria encompass study set-up, study deployment and analysis, and study outcome, stressing the importance of adopting a consistent terminology and result reporting style. The developed critical review highlights several shortcomings in the design, deployment, and documentation of multi-domain studies, emphasizing the need for quality improvements of future multi-domain research. The ultimate goal of this work is to consolidate our knowledge on multi-domain exposures for its integration into regulatory resources and guidelines, which are currently dominated by single-domain knowledge.

The perception, physiology, behavior, and performance of building occupants are influenced by multi-domain exposures: the simultaneous presence of multiple environmental stimuli, i.e., visual, thermal, acoustic, and air quality. Despite being extensive, the literature on multi-domain exposures presents heterogeneous methodological approaches and inconsistent study reporting, which hinders direct comparison between studies and meta-analyses. Therefore, in addition to carrying out more multi-domain studies, such investigations need to be designed, conducted, and documented in a systematic and transparent way. With the goal to facilitate and support future multi-domain studies and their meta-analyses, this work provides (1) a range of criteria for multi-domain study design and reporting (i.e., defined as quality criteria), and (2) a critical review of the multi-domain literature based on the described criteria, which can serve as guidelines and recommendations for future studies on the topic. The identified quality criteria encompass study set-up, study deployment and analysis, and study outcome, stressing the importance of adopting a consistent terminology and result reporting style. The developed critical review highlights several shortcomings in the design, deployment, and documentation of multi-domain studies, emphasizing the need for quality improvements of future multi-domain research. The ultimate goal of this work is to consolidate our knowledge on multi-domain exposures for its integration into regulatory resources and guidelines, which are currently dominated by single-domain knowledge.

In this study, we developed a method to identify the affecting parameters that can characterize the view-out in relation to different solar-control systems. We hypothesize that the photometric composition, e.g., contrast, in the visual environment as a result of using solarcontrol systems impacts the subjective assessment of the view out. To test our hypothesis, we conducted user assessment studies where we measured objective photometric measurements and subjective human responses in a real semi-controlled environment. The user-assessment study was done with 51 participants. The participants were randomly allocated to a combination of five views out and six solar shading systems in a work environment where they answered questions related to theindoor environment and view quality. The relation between view and solar-control systems and their impact on the subjective assessment of view out was tested through the development of linear mixed-effects models. The models were developed using forward and backward selection and likelihood ratio test (LRT) to test the effects of adding or removing variables. Photometric quantities were measured as achromatic contrast and calculated both locally and globally, which are two different algorithms for the calculation of contrast as perceived by humans. Both were found to be significantly associated with respectively view rating and satisfaction and could be used to characterize the viewout quality through commonly used shading devices.

Glare is a common local visual discomfort that is difficult to identify with conventional light sensors. This article presents an artificial intelligence algorithm that detects subjective local glare discomfort from the image analysis of the video footage of an office occupant’s face. The occupant’s face is directly used as a visual comfort sensor. Results show that it can recognize glare discomfort with around 90% accuracy. This algorithm can thus be at the basis of an efficient feedback control system to regulate shading devices in an office building.

Current discomfort glare prediction metrics usually account for at least one of the two categories of effects that induce discomfort glare – the saturation and contrast effects. Saturation-driven metrics (overall illuminance on the eye) are suited for brightly lit scenes in general. On the contrary, contrast-driven metrics (luminance ratio in the field of view) usually perform better in high contrast conditions such as with small-sized bright glare sources. Only a few existing metrics consider both effects, such as the Daylight Glare Probability (DGP). However, even these “hybrid” metrics may underperform in conditions other than those considered when they were developed, such as in dim scenes with high contrast glare. This paper investigates the ability of current glare indicators to predict perceived discomfort glare in user-evaluated scenes depending on two different adaptation levels. Towards this end, we used a composite dataset of six laboratory studies performed previously and separately in various parts of the world. According to Receiver Operator Characteristics (ROC) findings and complementary statistical research, the hybrid metrics DGP and Eccologit perform best in both investigated ranges (dimmer and brighter scenes). For the single-effect metrics, the contrast-driven metrics appear to perform better than saturation-driven metrics in lower adaptation levels (dimmer scenes), while the reverse is seen in higher adaptation levels (brighter scenes). As a result, metrics that only consider one effect should be used with caution. Although hybrid metrics continue to perform well in the investigated scenes, further research is needed to extend their applicability to a larger variety of lighting conditions that may be observed in work environments.