Influence of Light Reflection from the Wall and Ceiling Due to Color Changes in the Indoor Environment of the Selected Hall

Abstract

The main goal of this paper is to evaluate the effect of color changes on the interior surfaces of a selected hall on the level of daylight, represented by the DF factor. A single-story hall was chosen as the reference building, in which daylight falls through the side windows and a skylight at roof level. Measurement of the level of daylight in the real state of the building (in situ) was carried out. The measurement took place when the external boundary conditions of the measurement were met (external state of the sky). A survey was conducted among users, in which they considered the visual perception of the environment and what colors would be suitable for the walls, ceiling, and floor in the working environment of the hall. The evaluation of the respondents who considered the color of the floor was interesting, and several agreed that the floor should be brown. After debugging the model for the simulation based on the actual state of the measurement, simulation calculations were performed with selected surface colors in the interior of the hall. Computational simulations were performed for changing calculation boundary conditions. Daylight Factors (DF) (%) were evaluated, namely minimum, maximum, and average DF values for 15 selected simulations. The calculations were performed in the RADIANCE simulation program. Simulations included the change in the surface color of the simulated wall and the current ceiling surface color, the color of the simulated ceiling surface and the current wall surface color, and the color of the simulated wall and ceiling at the same time. The floor color did not change during the evaluation; it was considered brown. Based on the evaluation of AHP, evaluations of the significance and comparability of colored areas were performed. The value of the average DF was chosen as the most important, the less significant minimum DF value was chosen, and the maximum DF value was considered in the last place. The results show that white, gray, green, or yellow walls, white ceiling, and brown floor were rated as the most suitable for the interior surfaces in the considered hall.

1. Introduction

Daylight affects the use of the interior of buildings for residents throughout their lives. The conditions for creating daylight are diverse and include issues of aesthetics, health, the comfort of the population, energy savings for lighting, and landscaping [1,2,3]. The use of daylight simulation has expanded considerably as a necessary step to evaluate daylight in buildings accurately [4,5]. The use of artificial lighting can affect the overall energy consumption directly by increasing energy consumption or indirectly by increasing or decreasing the cooling/heating load [6,7]. Using daylight instead of artificial lighting can bring significant energy savings, between 30 and 70% of electricity consumption [8]. Similar information on daylight, artificial lighting, and energy-saving can be found in several publications [9,10,11,12,13]. Daylight also improves the quality of the premises and the productivity of employees in the work environment. It is known to have a positive effect on human health as well as on performance and productivity [14,15,16].

Daylight is an important requirement for improving comfort in an industrial environment [17]. The industrial and labor sectors consume large amounts of energy for daily production activities. It is also necessary to pay attention to the design of industrial construction. It brings new meaning and wealth to architecture. It allows architects to economically justify other visual elements that enrich the overall design [18,19,20].

Buildings that provide a high level of natural light generally have a more positive work environment than those that depend on artificial light. Humans are known to respond better to natural light conditions as their eyes and brains function better, leading to improved concentration and overall performance [21,22,23]. The introduction of skylights reduces dependence on artificial lighting and significantly reduces energy consumption and operating costs, as well as positively affects the overall carbon footprint of buildings [24,25,26]. In the case of large buildings, it is possible to use light pipes instead of artificial lighting [27,28].

Color is an essential element of interior design and influences the subjective visual perception of light. Color is associated with psychological, physiological, and social reactions. A color solution for a particular space may depend on several factors (type of activities, nature of light sources, size and shape of the space, etc.). Color also helps people interpret and understand the physical environment [29]. A study [30] showed that electricity savings of up to 45% could be achieved by increasing the reflective properties of surfaces. According to [31], it was found that within reasonable limits, wall albedo changes have little effect on the daylight factor. The study [32] analyzed the relationship between surface color reflectance and lighting efficiency. This study found that the reflectiveness of the ceiling affects the performance of lighting systems. Increasing the reflectance of a ceiling has a direct, positive effect on the lighting and energy used in a building. Studies have shown that a ceiling with a reflectance of 0.89 versus 0.75 can increase light levels by 25% with indirect lighting, 18% with direct/indirect lighting, and up to 4% with direct lighting. Most studies show the effect of using white or very light-colored walls. Different orientations to the cardinal directions could have different colors, with north being the darkest and east and west the lightest.

For industrial interiors, workshops, or individual workplaces, it is necessary to develop a separate design for each project. This design or project should be developed by the architect in close collaboration with an industrial psychologist and lighting technician. When building new facilities or modernizing facilities, it is necessary to have a comprehensive design or project for color in the working environment. It should be emphasized that color is an integral part of the conditions of suitable workplace lighting. J. W. Goethe published the text The Doctrine of Colors in 1810, the principles of which are still applied today. The direct effect of colors gives the light environment a warmer or cooler appearance. The color appearance of natural daylight also affects the reflection of colors from the main areas of the room. The walls and ceiling of the production area should be light in color to achieve a high reflection factor [33,34,35].

As already mentioned, the conditions of lighting and visual comfort depend to some extent on the reflectivity of walls, ceilings, and equipment, i.e., on the color of the interior. When designing color schemes, it is, therefore, necessary to choose solutions that improve visibility and allow the details of objects in the work environment to be distinguished [36,37,38]. In practice, this means that under the influence of lighting (i.e., light intensity, color temperature, or spectral composition), some shades of colors on the surface of products and work surfaces change. Therefore, subtle shades of color, less saturated colors, and colors dimmed to white or light gray should be used. This fact affects the subjective perception of detail and the environment as a whole. This results in the effect of color treatment on the perception of heat and cold [39]. Thus, it can be stated that visual perception of colors is important for inducing a feeling of warmth or cold, which modifies the feelings caused by the heat–humidity microclimate. This modulation of the feeling of warmth and cold in the perception of the color of the environment in humans is called the Hawthorn effect [40,41].

Under the influence of lighting, colored surfaces have a positive or negative effect on humans from a psychological point of view, and due to the given reflectivity, they also have a retroactive effect on the quantity and quality of lighting [42]. In a work environment where a visual task is performed, the brightness of the surfaces is very important. It is determined on the basis of reflectivity and illuminance of surfaces. Therefore, in order to increase the adaptation levels and visual comfort of users, it is more appropriate if the areas in the rooms are as bright as possible [43,44,45,46].

The choice of lighting system or type of light source also plays an important role. Light from light bulbs causes a gray color, while warm colors make it even warmer and more intense. Fluorescent lamps emphasize green, blue, and purple shades, while warm colors lose their intensity. It can be stated that the perceived color change is caused by halogen bulbs [47]. When choosing color shades (walls, ceilings, floor, structures, equipment, etc.), it is necessary to consider the type of predominant activity, size and shape of the space, color of processed objects, color and intensity of light sources, as well as overall microclimatic conditions [48,49,50]. In practice, it is not possible to take all these factors into account at the same time. According to the priorities of a particular workplace, it is necessary to sensitively choose those factors that have the greatest impact on the work process [51,52,53,54].

The aim of this paper is to analyze changes in the reflectivity of surfaces and to show how the surrounding surfaces affect the light climate during daylight. Light comfort is evaluated according to the daylight factor DF, which is calculated using dynamic simulations [55]. The RADIENCE simulation program is used to calculate the change in the light climate caused by changes in the light reflectance of interior surfaces.

In order to reduce the light load and meet the objectives set by regulations and standards, architects are advised to make maximum use of paints with high reflectivity on interior surfaces. Although this theoretically improves daylight factors and reduces the energy consumption of lighting circuits, it can limit selection and run counter to aesthetic desires. The authors Simm, S., and Coley, D. examined the relationship between surface albedo changes and daylight factor changes for typical non-domestic areas [56]. They counted only the part of the surface that was not covered. For example, in schools, the walls are covered with objects such as notice boards, blackboards, paintings, etc. The extent of such areas is estimated on the basis of building surveys. It was found that within reasonable limits, wall albedo changes have little effect on the daylight factor. Despite the above findings, we conducted a survey in the hall, where the walls are not covered with any tiles, boards, or other surfaces. The authors [57,58,59] focus on the indoor environment in industrial buildings, where the walls are without any covering by paintings, blackboards, and notice boards, as is the case in schools or office buildings.

2. Materials and Methods

2.1. Effect of Colors in the Workplace

When designing the color scheme of a workspace, it is necessary to take into account the nature and duration of the activity performed at the workplace (according to [60]) and whether the work is physical, mental, monotonous, or burdens the eyes, etc. Next is the shape and size of the space and the temperature in the workplace. The appropriate use of colors can alleviate psychologically unsuitable temperature conditions in the workplace. With a suitable color solution, problems with unsuitable lighting can be partially solved. In this case, colors are selected according to their ability to reflect light. As white shades are the most suitable for maximum light reflectance in terms of lighting efficiency, ceilings and ceiling lintels in industrial halls should only be painted white. However, from a maintenance point of view, this is not the most suitable. Load-bearing metal ceiling structures should have a light gray coating. The composition of workers by age and gender is important.

The color scheme of the workplace is not an end in itself, but it has several meanings. (See Figure 1). A functional definition is important. This means using colors to orient the worker in the workspace quickly and to reliably handle the necessary operations—functionality. This contributes to creating a working atmosphere appropriate to the nature of the work performed. It is also of economic importance, which is reflected in increasing labor productivity and reducing workplace accidents. No less important is safety, which consists of signaling safety and eliminating hazards in the workplace. The aesthetics—color aspect of the workplace is also necessary in terms of design.

The research, in this case, includes daylight measurements and simulations using the RADIANCE simulation tool. The result is the effect of changing the colors of the walls and ceiling on the indoor environment in the hall. Based on the above, the research can be characterized by the following flowchart of the solution (see Figure 2).

The effect of surface color on daylight in an industrial hall was studied.

Table 1. Psychophysiological effect of colors.

Group of Colors	Colors	Influence	Application
Warm colors	Red, yellow, orange, and their shades	They encourage, stimulate action, act on short-term increase, and increase performance	Where work is done mainly at night and in rooms that face north and north-west through windows
Cold colors	Green, blue, blue-green, and their shades	Soothe, provide visual relief, promote mental concentration, and maintain constant performance	In work areas where excessive temperatures occur, e.g., bakeries
Neutral colors	White	Brightens and expands the space, induces a feeling of harmony and peace, and improves mood. It can be combined with all other colors	Suitable for painting ceilings and ceiling lintels
	Gray	Neutral	Metal ceiling structures, in the background (it does not interfere and at the same time objects or devices penetrate it well), suppresses objects that disturb the space
	Black	Reduces space	-
Shades		Influence
Bright color shades		They brighten the workspace and improve the lighting conditions in the workplace due to their reflectivity
Dark color shades		They have a heavier to tight impression, and they dampen the reflectivity of light
Color saturation		Influence
Rich and colorful colors		They stimulate feeling and mood, and they enliven the space
Little rich colors		Soothe, create a color-balanced space

Note: The optimal choice of color for the ceiling, walls, and floor should respect the ability of each color to reflect light.

documents the psychophysiological effect of the application of different colors [60].

Recommended values of light reflectance from individual surfaces are shown in Figure 3.

The study ends with an analysis of computer simulations of several design variants. RADIANCE software was used for daylight simulations. The effects of surface color on daylight were simulated for the actual surface color, which was measured in the hall, and for 14 surface colors. Simulations were based on a cloudy sky.

2.2. Case Study–Hall Building

One reference hall of an engineering workshop was selected for the study. The hall location is at “Komenskeho Park” in Kosice, Slovakia. The studied production hall is a single-floor shop hall of 15 m × 60 m and height of 8.5 m with a total floor area of 9000 m² (see Figure 4a,b). The hours of the building’s production are from 8:00 to 16:00, Monday to Friday. The requested target illuminance is 500 lx at points located 850 mm above the floor.

There are two natural light sources in the hall, wall windows and skylights. For all windows, wire glazing with Visible Transmittance of 61% was used (windows) and 36% for skylights. Skylights were placed at the peak of the building for the length of the hall, 48 m. A straddle-type skylight was used with a width of 2.4 m at a height of 1.1 m above the roof (see Figure 4c,d). Side windows had dimensions of 5.6 m × 1.8 m (north and south) and 3.0 m × 1.8 m (east and west).

This study includes measurements of the lighting levels on the control points in the hall (see Figure 5a). The neighboring objects at a distance did not shade the hall.

The hall is used for medium-precision production and was classified in the III–IV light–technical class according to standard prescriptions. With the given lighting system, at the critical point of the functional place on the horizontal plane, the following values are required:

minimum standard value of daylight factor DF_min = 1.5–2%,

average daylight factor DF_average = 5.0–6.0%,

uniformity of the illumination more than u = 0.2

for the given visual task [61,62].

In a hall environment where a visual task is performed, the brightness of the surfaces is important. It is determined based on the reflectivity and illuminance of surfaces. The recommended reflectivity of surfaces and the reflectivity of some colors are given in

Table 2. Recommended reflection factor of surfaces, recommended reflection factor of colors, building material, and optical properties of the interior surfaces of the test hall.

Structural Surface	Reflectance/Transmittance	Color of Surface	Reflectance	Structural Surface	Optical Properties Reflectance
Ceiling	0.7–0.9	White	0.75–0.89	Floor	10%
Walls	0.5–0.8	Yellow	0.44–0.78	Windows	40%
Floor	0.2–0.4	Brown	0.12–0.45	Skylight	64%
Furniture, facilities	0.2–0.4	Gray	0.15–0.67	Facilities	20–50%
Windows	/transmittance 0.6	Black	0.02–0.04	Trusses	30%
Skylight	/transmittance 0.36

[63]. Figure 5 shows the floor plan and schematic section of the hall. A total of 60 points are marked in the floor plan 12 × 5 (A, B, C, D, E). Measurements were made at these points and were considered for computational simulation for individual color change variants. Calculated values of the daylight factor DF (%) in the hall from measured values of interior illuminance-current state can be seen in Figure 6.

During the creation of the computational model of the hall, boundary conditions were created on the basis of measurements in the hall with a saddle skylight [64]. The light reflectance values and properties of the materials used in the calculations are given in Table 2. The individual measured material constants (surface reflection factors, contamination factors, and glazing permeability) were also taken into account. Different surface colors (walls and ceiling) were used in this study; the floor is the same in all cases in this hall building (see

Table 3. Reflection factor for variants of surface color.

No.	Variant of Surface Color	Walls	Ceiling	Floor	RAL Walls Color
0	Current case	0.7000	0.7000	0.2000	RAL 7000 gray squirrels
1	Sim1	0.5600	0.7000	0.2000	RAL 7035 light gray
2	Sim2	0.7130	0.7000	0.2000	RAL 1013 pearl white
3	Sim3	0.5238	0.7000	0.2000	RAL 1021 mustard yellow
4	Sim4	0.5770	0.7000	0.2000	RAL 6019 green
5	Sim5	0.8380	0.7000	0.2000	RAL 9003 signal white
6	Sim6	0.5422	0.7000	0.2000	RAL 1023 traffic yellow
7	Sim7	0.3136	0.7000	0.2000	RAL 1026 bright yellow
8	Sim8	0.5238	0.5238	0.2000	RAL 1021 mustard yellow
9	Sim9	0.7130	0.7130	0.2000	RAL 1013 pearl white
10	Sim10	0.5600	0.5600	0.2000	RAL 7035 light gray
11	Sim11	0.8380	0.8380	0.2000	RAL 9003 signal white
12	Sim12	0.5770	0.5770	0.2000	RAL 6019 green
13	Sim13	0.5422	0.5422	0.2000	RAL 1023 traffic yellow
14	Sim14	0.3136	0.3136	0.2000	RAL 1026 bright yellow

). To increase the adaptation levels and visual comfort of users, it is more suitable if the surfaces in such a large hall are bright. For this reason, shades of white, yellow, and light green were chosen for the simulation. Calculations were performed for a set of different color rendering variants. There were 15 variant calculations, t. j. actual state (the real state in reality) plus 14 calculation variants of simulations (see Table 3). The factual state was determined by a concrete measurement in the real state.

2.3. Experimental Measurement

Daylight measurements were performed according to the Slovak standard “Daylight measurements” [65]. Two instruments were used in the measurements, namely:

(a)

lighting meter, CHROMA METER CL 200 Konica Minolta (serial number: 750366034), with an accuracy of 2%;

(b)

a brightness meter was used to measure the gradation of the brightness of the sky before and after the experimental measurement in the hall. It was a “Luminance meter LS-110”, with an accuracy of 2%.

The following were considered:

-

conditions of gradation of sky brightness on one considered day in November 2020 from 10:00 to 11:25;

-

conditions of sky brightness gradation on one considered day in December 2020 from 10:40 to 00:10;

-

the conditions that were found before and after the measurements in the direction S-north, V-east, J-south, and Z-west by the ranges of the L_e/L_vz ratios have changed;

-

the data document that the brightness distribution of the outdoor sky differed slightly from the CIE cloudy sky pattern during the daylight measurements indoors. It is so that:

-

the permissible variance of the relative brightness of the sky at a given altitude to the brightness of the sky at the zenith for 15% was 0.35–0.65, and for 45%, it was in the range 0.75–0.90.

Daylight measurements were taken on days when the artificial lighting was switched off. The reference points during the measurements were placed on a plane located 850 mm above the floor of the hall because the need for visual force is at a given height. Measurements were performed at 60 reference points and repeated 4 times. The number of measured points is due to the equipment of the hall. It was not possible to measure in these areas.

-

The value of outdoor lighting in cloudy skies ranged from approx. 5500 lx to 9000 lx in November 2020 and from approx. 4000 lx to 9000 lx in December 2020.

-

Light losses expressed by normal light transmission were found: τ = 0.6 (window glazing) and τ = 0.36 (skylight). The light reflectance coefficients of the main surfaces were determined as follows: ceiling ρ = 0.7–0.9, walls ρ = 0.5–0.8, and floor ρ = 0.2–0.4. These values (transmittance and reflectance) were measured with a standard brightness meter directly in real conditions. The equipment used can be seen in Figure 7.

This study consisted of several steps. First, specific measurements of the level of daylight in the hall were taken. Subsequently, 14 surface colors were selected as representative samples for the whole set. Selected variants and the current state were subjected to a simulation calculation using the RADIANCE lighting simulation software. Finally, a statistical evaluation of the results was performed, and the significance and impact of the color change were determined.

2.4. User Opinion

A survey of 57 respondents (28 women and 29 men) aged 20–25 years was conducted to select suitable combinations for simulations and subsequent statistical evaluation. They were asked various questions about the working environment, including questions about the color scheme of the surrounding surfaces where they work. They presented interesting opinions and facts. When adapting the interior, most would change the color of the surfaces in the following order: 1. floor, 2. wall, and finally 3. ceiling. The colors of the surfaces are important for them: 1. walls, 2. floors, 3. ceiling. The ceiling is listed last due to the fact that it is not directly in the field of view of the observer. The worker is most aware of the walls and the floor. As for colors, they preferred: ceiling, white; walls, gray; floor, brown. They stated the least suitable colors are: ceiling, yellow or green; walls, orange; floor, yellow. These colors would not be imaginable for creating surfaces in the work environment.

2.5. Analysis of the Obtained Data Using Selected Methods

For determining the weights and the resulting model, the Analytic Hierarchy Process (AHP) method was used. The AHP method to support multi-criteria decision-making was originally developed by Thomas L. Saaty. The basis for decision-making is empirical decision-making criteria, and using AHP outlines the whole decision problem as a hierarchical structure [66]. AHP is based on the value of the information obtained and derives ratio scales from paired comparisons of criteria [67] to discover and correct logical contradictions. Questionnaire surveys, in general, contain the subjective opinions of respondents. These opinions were taken as inputs and divided according to alternatives A₁ … A_m (competencies) and specific criteria K₁ … K_n (abilities of project managers). This process allows translating these opinions into measurable numeric relations.

Internationally, AHP is used in a wide range of applications, for example, for the evaluation of suppliers, in project management, or for the selection of the best alternative in a decision tree. AHP helps us make decisions in a more rational way and make them more transparent and more easily understood (Goepel, 2018). Using AHP as a supporting tool for decision-making helps gain a better insight into complex decision problems. It means the selection of the best alternative in the decision tree. As you need to structure the problem as a hierarchy, it forces you to think through the problem, consider possible alternatives (decision criteria), and select the most significant criteria with respect to the decision objective.

The criteria matrix for evaluation alternatives according to criteria is represented by Y = (y_ij), where rows are alternatives and columns are criteria. For every criterion, it was necessary to calculate its weight (number from 0 to 1). The more important criterion, the higher its weight (denoted w_j for criterion K_j, j = 1 … n). Some of the methods of criteria weights:

Method of entropy: no preferences, weights of criteria are equal (w_j = 1/n);

Method of order and Fuller’s method: ordinal information about criteria is known;

The Scoring Method and the AHP Method (Saaty’s method) [61]: ordinal information and distances between criteria are known. The scale of points for criteria is 1, 3, 5, 7, 9, where 1 = criteria are equal, 3 = the first criterion is more significant than the second one, 9 = the first criterion is absolutely more significant than the second one.

Mathematically, the AHP method [68] is based on the solution of an Eigenvalue problem. The results of the pair-wise comparisons are arranged in a matrix. Saaty’s matrix is squared, and consists of estimated elements $s_{ij}\approx \frac{w_i}{w_j}$ which are ratios of weights i-th and j-th criterion. Elements on the main diagonal are equal to 1. In an ideal case, for every i, j, k = 1, …, n should apply $s_{ij}=\frac{w_i}{w_j}=\frac{w_i}{w_k}·\frac{w_k}{w_j}=s_{ik}·s_{kj}$ and then this matrix is called consistent and reciprocal. So it could be multiplied from the right side by normalized right Eigenvector ${\left(w_1,w_2,\dots ,w_n\right)}^T$ (n is Eigenvalue) gives the ratio scale (weighting), the Eigenvalue determines the consistency ratio:

$$\left(\begin{array}{cccc}1& \frac{w_1}{w_2}& \cdots & \frac{w_1}{w_n}\\ \frac{w_2}{w_1}& 1& \cdots & \frac{w_2}{w_n}\\ \cdots & \cdots & \cdots & \cdots \\ \frac{w_n}{w_1}& \frac{w_n}{w_2}& \cdots & 1\end{array}\right)·\left(\begin{array}{c}{w}_1\\ w_2\\ \cdots \\ w_n\end{array}\right)=\left(\begin{array}{c}{w}_1\\ w_2\\ \cdots \\ w_n\end{array}\right)·n$$

(1)

In practice, it is very rare to obtain a fully consistent matrix.

3. Results and Discussion

An analysis of the results of light measurements in the selected hall, as well as improvements represented by changing the colors of the interior surfaces using RADIANCE software, was performed. This part begins by measuring the light parameters in the hall and those that were obtained from a direct simulation of the current situation performed by RADIANCE. An estimate of the accuracy of the simulated results was then determined. After this calibration/tuning of the model, the following cases were studied: (1) wall color change; (2) ceiling color change (same model); (3) simultaneous color change of walls and ceiling (same model). As mentioned above, the test study included measurements from 60 points in the hall. Subsequently, simulations for 14 different surface color combinations were performed at these points.

Table 4. Results of calculated DF by simulation program for variants of surface color.

	Variant of Surface Color	DFmin (%)	DFmax (%)	DFaverage (%)
0	Current Case	0.440	6.960	4.830
1	Sim1	0.239	6.899	3.170
2	Sim2	0.759	7.589	4.047
3	Sim3	0.615	7.269	3.822
4	Sim4	0.389	7.038	3.503
5	Sim5	0.670	7.254	3.849
6	Sim6	0.823	7.645	4.095
7	Sim7	0.881	7.655	4.315
8	Sim8	0.649	7.222	3.864
9	Sim9	0.695	7.421	4.053
10	Sim10	0.998	7.855	4.445
11	Sim11	0.268	6.720	3.289
12	Sim12	0.564	7.445	3.748
13	Sim13	0.386	7.127	3.450
14	Sim14	0.620	7.204	3.874

and Figure 8 show the calculated daylight factor (minimum, maximum, and average values of the daylight factor) for different surface color variants.

The room was divided into 5 bands A, B, C, D, E, and 12 rows, which equals 60 measuring and calculation points.

According to Saaty’s method, we determined the weights and the importance of the results obtained. The most important individual alternatives were the average values of the daylight factor DF, the less significant minimum DF values, and the least important maximum DF values. The maximum DF values can be reached at some points closest to the translucent openings, the lowest at the most shaded and remote locations, which may not affect the overall result. We chose the analytical hierarchy process weights in the ratio DF_average/DF_min/DF_max (%)

(i)

60/30/10

(ii)

70/20/10

(iii)

80/10/10

(iv)

50/40/10

(v)

50/30/20

(vi)

34/33/33

(vii)

90/7/3

The results for the calculation of all alternatives can be seen in Figure 9a), and ranked according to significance (see Figure 9b).

The most suitable solution is for sim10, followed by sim7, sim6, and sim2. The least significant are sim1, sim11, sim4, sim13, sim0 (current case), and sim12. Accordingly, the biggest differences are in the most appropriate solutions. As already mentioned, the most suitable, i.e., the most important, are sim10, sim7, and sim6 because their results are the most significant.

The smallest differences are in the middle from sim5 to sim12 and small differences from sim12 to sim1 (see Figure 9b). For these small differences, we present the calculation of DF simulations for the selected color combinations in Figure 10.

Here it can be seen that the results in the plan view and in the section are negligible for the selected variants of reflectivity (color solution).

4. Conclusions

The article deals with the daylight factor DF as one of the basic factors designed to assess the level of daylight in artificial material space. A single-story hall with a side window and a skylight at the top was chosen as the reference building. Activities were performed, namely, the measurement of the level of daylight in the real state of the building (in situ) when meeting the external boundary conditions of the measurement (reference state of the sky).

A survey was also conducted among 57 users who considered the importance of the color of the surrounding surfaces in the visual perception of the working environment. They also answered questions such as what colors they consider suitable for the color solution of the surrounding surfaces of the work environment (walls, ceilings, floors). Interestingly, the results indicated that the greatest impact would be the color of the floor, then the walls, and finally the ceiling. In terms of color, the most suitable would be a brown floor, light gray walls, and white ceilings. After debugging the model for simulation with selected real colors of the surfaces in the interior in situ, the boundary conditions of the walls and ceiling were chosen for the considered simulations. These conditions meant changes in color solution, that is, changes in light reflection factors. The floor color was considered brown with a reflectance of 0.2. This means that the floor color was constant throughout the simulation calculation, as chosen by the users. The simulations evaluated the daylight factors (%), namely the minimum, maximum, and average DF. A total of 15 simulations were performed, which were solved by the RADIANCE simulation tool. In each of them, a calculation (as well as a specific measurement in the interior real state) was performed for 60 points (5 times 12 lines).

The main motivation for this study was to determine the impact of different colors of the surrounding surfaces on the work environment. The simulations included changing the simulated wall surface color and the current ceiling surface color, the simulated ceiling surface color and the current wall surface color, and the simulated wall and ceiling surface color at the same time. The floor color remained unchanged. Standard lighting regulations affect human activity and the quality of work. In our conditions, it is normally 500 lx during normal work activities. According to the SK national standard, in terms of limited measurement accuracy, daylight is considered satisfactory if the results of measurements or simulations do not differ from the required values by more than 15%. This fact is not fulfilled. The calculated values document the fact that:

i DF_min is unsatisfactory in functionally defined parts of the space because the condition is not met. DF_min is 0.2–0.7, which is less than the required level of 1.5–2.0%.

ii Average DF assumes a share of ceiling lighting in the average value of DF of more than 5.0%; The calculated value of 3.1–4.8% is suitable for lighting-class IV.

iii The uniformity of daylight (U0) in the space of the combined lighting (windows, skylights) is met if the minimum and average value of DF is met. The uniformity of the lighting, in this case, is not satisfactory for lighting-class III or IV. When performing the activity, it will be necessary to supplement the daylight with artificial lighting in the place where the work activity will be performed. As mentioned, 15 computational simulations were performed in this work based on the AHP evaluation.

Classifications of the significance and comparability of color areas were performed. The results show that the simulated alternatives sim10 (walls and ceiling light gray), sim7 (walls bright yellow and ceiling white), and sim6 (walls traffic yellow and ceiling white) are suitable for the color of the surrounding areas of the working environment. This means that the best results are provided by the colors white, gray, and yellow with a brown floor. These colors were also positively evaluated by the hall users.