Correlated color temperature (CCT) thresholds are widely used in European lighting regulations to limit the ecological impact of artificial light at night in protected areas, yet CCT does not uniquely determine the underlying spectral power distribution. Prior studies have examined receptor-specific responses to CCT in isolation - human circadian stimulation, sky-glow potential, or insect phototaxis - but none has simultaneously quantified intra-CCT spectral variability across the full range of receptor systems ecologically relevant to a legally protected natural area. This study provides the first receptor-resolved, multi-metric quantification of intra-category spectral variability for heritage fa & ccedil;ade lighting in such a context. A Neo-Gothic monument adjacent to the Natura 2000-protected Knyszy & nacute;ska Primeval Forest, Poland, served as a case study. The spectral power distributions of commercial LED sources spanning 2200-6500 K were evaluated against a metal halide reference using 11 metrics encompassing human circadian response, scotopic-to-photopic (S/P) ratio and sky-glow index (SLI) for sky-glow potential, receptor-weighted effectiveness for nocturnal fauna, and phytochrome sensitivity for plant photoreceptors. Warm-white sources (2200-2700 K) consistently produced the lowest ecological impacts across all evaluated metrics; the S/P ratio and SLI were mutually consistent and confirmed that all warm-white CCT categories fall below the metal halide reference in sky-glow potential, in line with CIE PS 003:2025 guidance. The most critical finding is the magnitude of intra-category spectral variability: at the regulatory threshold of 3000 K, differences between sources sharing the same nominal CCT exceeded in several cases the ecological gap between entire adjacent CCT categories. Notably, the phytochrome metrics revealed a non-monotonic CCT dependence - a pattern invisible to CCT specification alone - demonstrating that plant photoreceptor impacts cannot be inferred from color temperature. These results demonstrate that nominal CCT compliance, even at values not exceeding 3000 K, provides only partial ecological assurance; verification of the spectral power distribution is necessary to guarantee meaningful environmental protection in ecologically sensitive heritage lighting applications.
The primary criterion in road lighting is to maintain the average luminance (Lavg) value within the ranges specified in the standards; in fact, insufficiency of these levels reduce visibility, whereas excessive levels cause glare, thereby compromising traffic safety. In current inspection processes, the necessity of stopping traffic and performing measurements at multiple points using a luminance meter makes the process time-consuming and operationally challenging. In this study, in order to overcome these limitations, a novel method based on the Adaptive-Network Based Fuzzy Inference System (ANFIS) which estimates the average luminance using only edge-of-road measurements and physical parameters without fully closing the road to traffic, or by applying partial closure when necessary is proposed. To validate the effectiveness of the proposed method, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as baseline performance references. It was demonstrated that the ANFIS model achieved superior prediction performance with a Mean Absolute Percentage Error (MAPE) of 0.71% on the test dataset, compared to ANN (1.62%) and MLR (4.11%). Within the scope of the current dataset parameters, this study provides a comprehensive preliminary validation for Lavg estimation with high accuracy. In other words, the proposed model represents a road-specific calibration framework and requires revalidation or recalibration before being applied to different road conditions. Additionally, the generalization capability of the model across different road geometries is planned to be evaluated in future studies using larger datasets.
Reliable, sustainable electricity in educational facilities is crucial for effective learning, as power outages and reliance on nonrenewable energy disrupt activities and harm the environment. In this manuscript, a sustainable energy management approach is proposed to optimize solar-powered Light Emitting Diode (LED) illumination in educational lighting systems. The proposed technique combines the Hamiltonian Deep Neural Network (HDNN) and Electric Eel Foraging Optimization (EEFO), which is referred to as the HDNN-EEFO approach. The primary aim of the proposed technique is to enhance energy efficiency, minimize operational costs, and ensure stable illumination under varying weather conditions. The HDNN is used to predict load demand and adapt to environmental changes, while the EEFO is employed to optimize LED driver energy consumption and system cost. The proposed method was implemented on the MATLAB platform and compared with other existing methods, including Process Parameters Optimization (PPO), Artificial Neural Network (ANN), and Particle Swarm Optimization (PSO). Compared to existing approaches, the proposed method achieves the lowest prediction error 0.1% and a reduced implementation cost of 1.2 cents/kWh. These results demonstrate that the HDNN-EEFO system is suitable for deployment in energy-constrained and off-grid educational institutions, enabling cost-effective, reliable, and environmentally sustainable classroom lighting.
This paper presents a new workflow for the generation of color and luminance calibrated high dynamic range (HDR) images. By investigating the relationship between some of the key generation steps along the path from light entering the camera to a pixel in the final image, the important sources of uncertainty are identified and mitigated as much as possible. This paper shows how adjustments to the HDR pipeline and additional calibration steps will lead to more consistent results that are robust across a wide range of broad-spectrum lighting conditions. These adjustments include: correctly setting the saturation point according to the raw sensor values, using a pre-calibrated shutter speed correction, merging to account for sensor-blooming and photon noise, demosaicing after merging to preserve details and avoid artifacts, and applying a calibrated color transform. Then, to cope with lens flare, one of the major sources of inaccuracies in capturing HDR images of scenes where the direct sun is visible, a novel process that employs a camera mounted shadowband is introduced. To demonstrate the practical application of this workflow, a pilot data set of hemispheric facade environment maps suitable for use in daylight simulation is presented, involving sky and sun illumination with highly detailed scenes of varied view content. This pilot data set of HDR environment maps is validated against chroma/illuminance and pyranometer measurements achieving a mean absolute percentage error of 6.2% from reference illuminance measurements with an average CIELUV chromaticity difference less than one-fifth of a just noticeable difference.
Light exerts hormonal, circadian, and behavioral influences beyond its visual function, yet consensus is lacking on the optimal illuminance and correlated color temperature (CCT) for various workplace tasks within the scope of integrative lighting. This study investigates and compares five lighting scenarios-one dynamic and four static-in a real office environment to explore the balance between visual comfort, biological effectiveness, and user preference. The static scenarios comprised horizontal desk-plane illuminance and CCT settings of 500 lx at 4000 K, 1000 lx at 5500 K, 1250 lx at 4000 K, and 1500 lx at 5500 K, while the dynamic scenario varied between 500-1000 lx and 3500-5500 K at the desk plane over the day. Lighting conditions were assessed through objective measurements, melanopic equivalent daylight illuminance calculations, and subjective surveys over short- and long-term exposures. The study found that high illuminance and cool light enhanced alertness and energy, but participants preferred neutral light with elevated illuminance. The scenario with 1250 lx at 4000 K was most favored, providing an optimal balance between visual and nonvisual effects. These findings guide integrative lighting design to enhance office comfort, well-being, and productivity.
This paper proposes a production workflow for creating real-time environment-adaptive media facades. To overcome the limitations of existing pre-rendered media facades, a real-time rendering system based on a Virtual Reality camera and a game engine was developed. This system captures the surrounding environment in real-time using a 360-degree VR camera, converts the captured footage into Emissive Light Material within the game engine, and dynamically reflects immediate environmental changes through real-time ray-tracing. The effectiveness of the proposed system was verified through experiments utilizing spherical and cube models, measuring changes in graphic expressions based on variations in light brightness and color temperature.
Lit areas are present in urban and suburban areas and in their surroundings. Yet, lighting tends to extend and increase in lit areas and probably in protected areas that conserve nature and species in Southeastern Europe. This study presents an overview of relevant studies on lighting with a focus on lit and protected areas of Albania, Croatia, Montenegro, North Macedonia, and Slovenia in Southeastern Europe, using the Google search engine on lighting pollution and biodiversity between 1992 and 2026, applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and LDAShiny open-source application. Lighting increases and decreases in the lit and protected areas were calculated for the five Southeastern European countries using remote sensing lighting data between 2020 and 2025, explaining these changes in lighting using review findings. The 17.8% relevant studies on lighting pollution and biodiversity tended to be at the European and global levels. Trends of studies were on "light" and "insects" in 2025 and 2026. The decrease in lighting between 2020 and 2025 was more present in protected areas in Slovenia and to some extent in Croatia than in Albania, Montenegro, and North Macedonia. There is a strong need for future studies on lighting in lit and protected areas in Southeastern Europe.
Light-emitting diodes (LEDs) have become the dominant lighting technology in museums and galleries, yet little is known about how they are currently implemented in practice. This study reports results from a nationwide online survey of U.S. museum lighting professionals examining lighting technologies, selection criteria, and standards use. Responses indicate that LEDs account for the majority of museum lighting systems, while legacy sources such as fluorescent, metal halide, and halogen lamps play a limited role. Lamp selection is primarily driven by conservation-related concerns, color appearance, and dimming capability, with energy efficiency considered secondary. Practitioners commonly reference multiple guidelines, including ANSI/IES RP-30, CIE 157, and Canadian Conservation Institute recommendations. The findings provide an updated snapshot of contemporary museum lighting practice and highlight ongoing variability in the application of standards, underscoring the need for clearer, harmonized guidance in the solid-state lighting era.
Modern classrooms face challenges not only due to the use of daylighting but also because of the various learning activities that take place within them. These challenges can be mitigated by implementing multizone lighting control systems (LCSs). However, the high cost of sensing and feedback systems hinders the widespread adoption of such solutions. This study introduces a low-cost multizone LCS that utilizes an IP camera as a vision sensor and several Arduino Uno units as microcontrollers. This system was implemented in a multiuse classroom accommodating various types of learning activities. The experimental results demonstrate that the LCS effectively maintains the luminance level within the desired range in the classroom, even with fluctuating outdoor daylight conditions. Additionally, it achieves a reduction in energy consumption ranging from 30% to 78%. The integration of this sensing feedback technology indicates a positive direction for future development.
Cone fundamentals can be derived from multiple pairs of color-matching experiments using light sources with varying peak wavelengths. While more matching pairs generally improve the accuracy of LMS reconstruction, they also increase experimental duration. This study addresses the challenge of improving experimental efficiency in color matching while maintaining the accuracy of LMS reconstruction. By introducing the overlap range ratio (ORR) as a metric to evaluate reconstruction performance, we systematically analyzed the effects of matching types, initial color configurations, and the number of matching pairs on the reconstruction accuracy. The results demonstrate that combining Moreland and White matching types yields superior LMS reconstruction performance. Additionally, using different initial color configurations within a certain matching type significantly enhances reconstruction accuracy, while variations in color ratios have no significant impact. Moreover, the minimum number of experiments required depends on the required reconstruction accuracy; for example, achieving an ORR > 0.8 requires at least 10 experiments. The findings provide valuable insights for optimizing LMS reconstruction experiments, balancing efficiency and accuracy, and have significant implications for the clinical application of multi-color pair matching in future color vision assessment experiments.
Preferences for correlated color temperatures (CCTs) are often used to guide lighting design, yet there liability of psychophysical methods to measure them remains uncertain. Central tendency bias, in particular, may systematically distort reported "preferred" CCT values, but its effect across different psychophysical methods has not been well established. This study examined two psychophysical approaches, paired comparisons (relative scaling) and semantic differential scales (absolute scaling), across three semi-overlapping CCT ranges. Twenty-four naive participants completed all experiments under controlled laboratory settings. Paired comparison data were analysed with a generalized linear model and semantic differential data with non-parametric repeated-measures tests. The experiment was designed to determine (i) whether preference peaks shift when the tested CCT range changes and (ii) whether absolute and relative scaling methods produce comparable preference functions.
Colorful commercial signboards are a prominent source of nighttime light exposure in urban environments, with measurable effects on both visual (photopic) and nonvisual (circadian) systems. This study presents an integrated approach combining field measurements and spectral lighting simulations to quantify these effects and support evidence-based strategies for mitigating obtrusive light. A total of 124 signboards were assessed through luminance-based validation, and 45 storefronts were evaluated using illuminance-based validation. Simulations were performed using three-channel and nine-channel spectral methods implemented via Radiance and Lark, respectively. The simulated photopic and circadian values agree with field data, with relative mean bias errors below 4% and relative root mean square errors of approximately 20%. The validated workflow is subsequently applied to explore design parameters for minimizing circadian disruption. Recommendations on signboard area, maximum allowable average luminance, and spectral composition based on building-to-signboard distance are proposed. These findings provide a replicable methodology for assessing circadian-effective lighting from commercial signboards and offer practical guidance for the spectral and spatial optimization of signboards in urban lighting design.
The existing CIE color matching functions (CMFs) consider mainly the influence of age and fields of view (FOV). However, some mismatches occurred when calculating colors from different FOVs in modern display systems. A color matching experiment was conducted to investigate the performance of CMFs between 2 degrees and 32 degrees fields of view (FOV) with different displays. The goal was to find the proper CMFs to characterize the color matches for displays with different primaries at corresponding FOVs. Accordingly, three CMFs, recommended for the FOVs less than or equal to 4 degrees, between 4 degrees and 10 degrees, and beyond 10 degrees can give better agreement to the visual results, regardless of the primary sets of the reference and matched displays. The results also showed that when the FOV increased beyond 10 degrees, there was no significant difference between the calculated results and those from larger (i.e. 32 degrees) FOVs.
In applying the concept of lighting design objectives (LiDOs) procedure, a lighting designer shall specify the mean room surface exitance (MRSE), which serves as the indicator of spatial brightness of the room. To guarantee that the specified MRSE can be achieved in the actual result, recent literature suggested that the designer may choose the desired target/ambient illuminance (TAIR) for each surface and object, except for the last (n-th) surface, which must be computed individually. However, the choice of which surface to be assigned as the last surface may yield different design outcomes and may, at times, render the design impractical. This brief report aims to observe the impact of choosing different surfaces as the last surface in a hypothetical room and to compute the probability of having an impractical design outcome. Monte Carlo simulation is applied to compute the output of LiDOs procedure in 1,000 random combinations of specified MRSE, room sizes, and surface reflectance values. It is recommended to assign the ceiling, whose reflectance is typically the highest in the room, as the last surface in LiDOs procedure, since it gives the lowest probability of having an impractical design outcome.
Supervised machine learning (ML) is emerging as a powerful tool for handling complex multivariate data sets and capturing nonlinear relationships between predictors and outcomes. Unlike traditional statistical techniques that only enable the identification of predefined functional relationships, supervised ML methods can robustly detect intricate nonlinear interactions without explicit assumptions. The capacity for integrating diverse predictors makes these tools particularly suitable for challenges for which traditional modeling shows limitations. Prediction of discomfort due to glare is one such case, where current models often fail to explain variance in data. These models mostly focus on objective factors related to the scene, while neglecting subjective factors that can vary between- and within-users. These limitations contribute to significant residual variance between model-predicted and reported discomfort scores, which cannot be explained without accounting for inter- and intra-individual differences. To improve these predictions, this paper proposes a method based on supervised machine learning techniques where multiple predictors and their nonlinear relationships are integrated towards improving model accuracy and robustness. Via preprocessing, features of a data set collected from a lighting experiment were refined, and prediction models were developed using eight state-of-the-art ML algorithms. Through inverse regression, the best performing model was further deployed for optimizing the characteristics of delivered lighting, ensuring that user perception was maintained within a target zone when all other variables were constrained. Our method demonstrates a practical approach for data handling, multivariate nonlinear modeling, and user-centered optimization of delivered lighting, with potential application for problems beyond prediction of discomfort due to glare.
Illuminance imaging based on lux sensor measurements has emerged as an effective alternative to camera-based methods, particularly in environments where optical imaging is impractical or limited. Converting lux data into illumination images provides a lightweight means of visualizing spatial lighting patterns; however, reliably classifying these images remains challenging due to variations in indoor geometry, sensor motion, and non-uniform lighting conditions. These factors often reduce the accuracy of conventional similarity or distance-based classification approaches. This study investigates 2 histogram-driven techniques for classifying real-time illumination images: the histogram intersection method and the histogram Euclidean distance method. A wireless sensing platform was developed by integrating an illuminance sensor with an infrared transceiver and a microcontroller communicating via the I-2 C protocol. As RF-operated mobile unit traversed the indoor test environment, continuous lux measurements were acquired and converted into grayscale illumination images for subsequent analysis. A comparative performance assessment demonstrates that the histogram Euclidean distance method delivers higher classification accuracy and greater consistency than the Histogram Intersection approach. The results highlight the potential of histogram-based lux-to-image classification as an efficient tool for real-time illuminance assessment and support its broader applicability to smart lighting, indoor monitoring, and energy-aware lighting control systems.
A novel intelligent framework is presented that integrates supervised machine learning with a mathematical rule-based system to detect, localize, and diagnose electro-thermal faults in a network of multiple light-emitting diode (LED) luminaires. The proposed system employs a sensorless data acquisition system for cost-effectiveness. Based on the electrical data captured, a fault detector initially determines the presence of faulty luminaire(s) in the group. Once a fault is detected, a fault locator module pinpoints the exact faulty luminaire by referencing its serial number. Next, a fault diagnostician classifies the specific type of electro-thermal fault affecting the identified luminaire. If the diagnosed fault is open or short circuit, an additional logic block is activated that specifies the faulty LED string within that luminaire. The intelligent framework employs two machine learning models: logistic regression for fault detection and extreme gradient boosting (XGBoost) for fault diagnosis. It is found that both ML models deliver high accuracy (>99%), strong generalization (generalization errors <= 0.8%), near-perfect correlation between the predicted and the actual data (Brier scores <= 0.0029) and rapid execution (<= 2.01 seconds). Multiple case studies show that the complete intelligent system demonstrates 100% effectiveness in fault detection, localization, and diagnosis which affirms its suitability for real-world deployment.
Luminescent solar concentrators (LSCs) offer a promising route for integrating photovoltaic technologies into buildings, providing both energy harvesting and architectural aesthetics. While extensive studies have focused on enhancing optical efficiency, visual properties such as color and transparency have received limited attention. This study presents a Monte Carlo ray-tracing approach to predict the color characteristics of LSCs incorporating mixed fluorescent dyes (LR305 and LY083). Simulated color coordinates, spectral transmittance, and color rendering index (CRI) were used to assess visual performance under daylight (AM1.5). A 40 ppm mixed-dye LSC achieved optimal trade-offs, with the predictive value CRI of 73 and transmittance above 58%, satisfying basic visual comfort requirements. A large-scale poly methyl methacrylate (PMMA)-based LSC panel was fabricated based on the simulation, and its optical properties were experimentally validated. The measured color coordinates and CRI (73.7) exhibited agreement with the predicted values. These results demonstrate that the visual performance of LSCs can be accurately predicted and optimized, enabling their integration into energy-efficient architectural applications.
