ABSTRACT In recent years, a progressive renovation of public lighting has been taking place in cities, from metal halide lamps to LED luminaires. This renovation has been carried out on a large scale by replacing some luminaires with others without taking into account other elements of the circuit. Due to the high number of electronic components, LED luminaires generate inrush currents that can exceed several times the nominal current value at ignition process. This electrical phenomenon is a basic problem to be solved when planning a street lighting renovation with LED luminaires, because it is essential to have a prior estimation of the inrush current generated by the luminaires for a correct sizing of the circuit protection elements. Currently, it is difficult to predict the value of the inrush current, for this reason, the main objective is to establish an innovative methodology based on advanced multi-criteria decision-making methods (entropy and CRITIC) to predict anomalous behaviors (inrush current peaks) in LED luminaire installations and their appropriate selection in the design phase or before are mounted in the circuit to avoid negative consequences on the components and to dimension them correctly, producing cost savings in the replacement of circuit elements such as circuit breakers and switches.

ABSTRACT Luminance uniformity of luminaires is an important design aspect that can affect perceived discomfort glare, luminaire efficiency, and visual satisfaction. There is, however, a lack of studies that evaluated the performance of different luminance uniformity metrics. This article presents results of two studies where luminance patterns were presented via online questionnaires and subjective ratings of uniformity were collected. Study 1 examined the performance of a uniformity metric based on the human visual system (UHVS) using a priori hypotheses, whereas Study 2 compared UHVS to four other metrics: Max:Min, Avg:Min, entropy uniformity (EU), and coefficient of variation (CV) using correlations and non-linear models. Of the metrics evaluated, UHVS performed best for predicting perceived luminance uniformity. In situations where a tradeoff between metric calculation simplicity and performance is acceptable, the use of CV is recommended.

ABSTRACT To limit glare, lighting engineers need to estimate the level of discomfort produced by a lighting installation. Outdoor, most discomfort glare models predict a mean level of discomfort on a judgment scale, which corresponds to a “mean road user.” However, in real life, the inter-individual variability of the sensitivity to glare is large; predicting the mean discomfort does not describe the full pattern of responses in a population. We propose a methodology to model the Discomfort Level, computing the probabilities that an observer reports any of the levels of a discomfort scale. This methodology is demonstrated for outdoor lighting, where no probabilistic model is available. Using a dedicated psychophysical experiment, we extend the results of a previous study where a formula was proposed to compute the Glare Excitation due to a multi-source stimulus, at the Borderline between Comfort and Discomfort (BCD), with a background luminance consistent with outdoor lighting. This formula is first extended to other discomfort levels and background luminance levels, and strengthened thanks to a different experimental protocol and to the use of up to 10 glare sources simultaneously. From this experiment, a formula of the Glare Excitation is proposed in the range of outdoor lighting. Then, a probabilistic model of the Discomfort Level is proposed, allowing computing the probability of ratings at each level of an ordinal scale from the Glare Excitation computed with our formula. It is hoped that the proposed model avoids most of the modeling methodological caveats of previous discomfort glare models.

ABSTRACT Daylighting standards provide an assessment method that can be used to evaluate the quality of window views. As part of this evaluation process, designers must achieve five environmental information criteria (location, time, weather, nature, and people) to obtain an excellent view. To the best of our knowledge, these criteria have not yet been verified and their scientific validity remains conjectural. In a two-stage experiment, a total of 451 persons evaluated six window view images. Using machine learning models, we found that the five criteria could provide accurate predictions for window view preferences. When one view was largely preferred over the other, the accuracy of decision tree models ranged from 83% to 90%. For smaller differences in preference, the accuracy was 67%. As ratings given to the five criteria increased, so did evaluations for psychological restoration and positive affect. Although causation was not established, the role of most environmental information criteria was important for predicting window view preferences, with nature generally outweighed the others. We recommend the use of the environmental information criteria in practice, but suggest some alterations to these standards to emphasize the importance of nature within window view design. Instead of only supporting high-quality views, nature should be promoted across all thresholds dictating view quality.

ABSTRACT The advancement of LED and controls technology, computing capacity, and software provides new opportunities for researchers and designers to work together to further optimize spaces for occupant benefit. Lighting system control data from five neonatal intensive care unit patient rooms was collected over a 25-week monitoring period and analyzed to better understand occupant response to a tunable lighting system with automatic transitions throughout the day. Lighting systems are very rarely refined after installation based on actual use. Objective data detailing how the lighting system is used by the actual occupants highlights the opportunities for optimization after installation and provides insight for improving the next design. As use of the data becomes more commonplace, it can be leveraged for design recommendations. The collection of the data required no additional cost beyond the time for examining the data. The analysis revealed several clear opportunities for improvement, including adjustments to the default control setting at night, re-labeling of the control stations, and adjustments to the nighttime fade rate. The patient room occupants were active users of the different zones, dimming options, and manual overrides made available by the lighting system.

ABSTRACT The blue light hazard (BLH) and luminous efficacy of a source () are associated with the photobiological safety and energy saving of light-emitting diodes (LEDs), respectively. In this study, we used genetic algorithm (GA) to optimize the BLH, , and color rendering parameters of five-chip hybrid white LEDs. Based on the optimal results, we used five-chip LEDs to obtain the hybrid white light. Their peak wavelengths were 461.5 (blue), 523.9 (green), 588.2 (orange), 643.3 (red), and 694.2 nm (far-red), respectively. In practice, the blue light hazard efficiency of radiation (BLHER) of the hybrid white light was less than 0.10, which was lower than half of the most common light sources. The ranged from 51.6 to 115.6 lm/W at correlated color temperature (CCT) from 2700 to 6500 K. On average, the CIE general color rendering index (Ra), CIE special color rendering index for the ninth deep red test sample (R9), ANSI/IES TM-30 color fidelity index (Rf), and color gamut index (Rg) of hybrid white light were 91.3, 87.8, 83.3, and 96.7, respectively. The melanopic efficacy of luminous radiation () was tunable in the range of 0.60 to 1.35 mW/lm. These results demonstrated that we used far-red LEDs to fabricate white light with low BLH and relatively high efficiency.

ABSTRACT This article describes a method for calculating and specifying light source chromaticity using the International Commission on Illumination (CIE) 2015 10° color matching functions (CMFs), which, according to analysis of existing psychophysical experiment data, can reduce visual mismatch compared to specifications based on the traditional CIE 1931 2° CMFs in architectural lighting applications. Specifically, this work evaluates, documents, and recommends for adoption by lighting standards organizations a supporting system of measures to be used with the CIE 2015 10° CMFs: a new uniform chromaticity scale (UCS) diagram with coordinates (s, t), a measure of correlated color temperature (CCTst), and a measure of distance from the Planckian locus (Dst). It also presents options for updating nominal classification quadrangles. A complete method of this nature has not yet been standardized, which may be contributing to the slow uptake of the CIE 2015 CMFs. The proposed tools are analogous to u, v, CCT, Duv, and the American National Standards Institute (ANSI) C78.377 chromaticity specifications that are all currently defined in the CIE 1960 UCS diagram using the CIE 1931 2° CMFs. While conceptually equivalent, the differences between the current standard method and the proposed st system are important for reducing unintended visual mismatch in the chromaticity of light. The implications of changing chromaticity specification methods are identified by a comparison over a diverse set of real light source spectral power distributions.

ABSTRACT Spectrally and spatially resolved information on daylight is critically important when planning for non-image forming (NIF) responses. Nevertheless, the availability of such data is scarce given the high initial costs and complex on-site maintenance of high-end spectral measurement devices. The CIE (Commission Internationale de l’Éclairage) reconstruction procedure allows for the derivation of the daylight spectral power distribution (SPD) from the chromaticity coordinates or the correlated color temperature (). However, several studies have suggested that both the daylight locus and the reconstruction procedure are erroneous, and specifically SPDs with a higher cannot be reproduced accurately. This paper studies the reconstruction accuracy of the SPD of daylight, and contextualizes the findings in relation to NIF effects. The analysis comprises a comparative study to determine the accuracy of the CIE procedure compared to two localized reconstruction procedures, and a sensitivity study to examine the impact of accuracy on the assessment of NIF responses, as represented by all five retinal photoreceptors and expressed in the α-opic efficacy of luminous radiation. The results indicated that a localized procedure, adjusting both the daylight locus and the PCA components of daylight, outperformed the CIE reconstruction method. However, improvement in the reconstruction accuracy had no effect on NIF assessment. The RSMPE for α-opic quantities did not exceed 4% for any procedure. In practical terms, this implies that cost-effective sensors and the representation of spectral properties in sky models with a single value – the correlated color temperature – can be used for NIF purposes. These findings bridge theory and practice by opening up new insights into the understanding of simplified methods used to determine NIF effects of daylight.

ABSTRACT The lamp spectrum and the surface reflection both contribute to the luminous efficiency of the illuminated object. Hence, it may not be comprehensive to evaluate the luminous efficiency in a lighting environment only through the metrics with respect to lamps. Previous studies have investigated the effect of surface reflection on luminous efficiency, but few quantitative metrics have been proposed so far. In this study, statistical analysis and a validation experiment were conducted to investigate the extent to which the luminous efficacy of radiation (LER) determines the luminous efficiency of a colored surface. The correlation analysis was performed based on an index of the surface spectral reflectance, namely, the spectral reflectance luminous efficacy of radiation (SRLER). The correlation between the SRLER and LER varied with respect to the hues and saturation levels of the colored objects. In particular, highly linear correlations were observed in slightly saturated and highly saturated green, yellow, and purple-blue samples but not in highly saturated reddish samples. This suggests that when the illuminated environment contains a large portion of highly saturated reddish objects, the effectiveness of the LER is dependent on the spectral selectivity of the colored surface. The findings of this study contribute significantly to the comprehensive design of light source spectra and energy saving in actual lighting scenarios.

ABSTRACT Most people with low vision rely on their remaining functional vision for mobility. Our goal is to provide tools to help design architectural spaces in which safe and effective mobility is possible by those with low vision – spaces that we refer to as visually accessible. We describe an approach that starts with a 3D CAD model of a planned space and produces labeled images indicating whether or not structures that are potential mobility hazards are visible at a particular level of low vision. There are two main parts to the analysis. The first, previously described, represents low-vision status by filtering a calibrated luminance image generated from the CAD model and associated lighting and materials information to produce a new image with unseen detail removed. The second part, described in this paper, uses both these filtered images and information about the geometry of the space obtained from the CAD model and related lighting and surface material specifications to produce a quantitative estimate of the likelihood of particular hazards being visible. We provide examples of the workflow required, a discussion of the novelty and implications of the approach, and a short discussion of needed future work.