An Evaluation of the Luminous Performance of a School Environment Integrating Artificial Lighting and Daylight
1. Introduction
In this sense, this article aims to present the evaluation results of an improvement performed in a classroom. An artificial lighting system was retrofitted with LED lamps, in addition to an increase in the use rate of natural lighting with the simple routine procedure of an artificial lighting control that would be executed by the users, neither demanding complex nor expansive control systems, legitimating its implementation.
The classroom is inside a building at the Federal University of São Carlos—UFSCar, São Carlos and Sao Paulo State, Brazil. The evaluation was performed by means of the DIALux evo software (Version 5.8.2.41968), and based on the simulation results, a new lighting system associated with manual lighting control was proposed in order to improve the luminous performance and the quality of lighting with less electrical energy consumption.
2. Materials and Methods
The Federal University of São Carlos has 126 buildings for academic purposes. These buildings are categorized by function as management buildings, academic departments, libraries, auditoriums, restaurants, and classrooms. There are specifically 7 buildings that are used as classrooms for an average of 10,000 students, denominated AT1 (built in 1997), AT2 (built in 1994), AT4 (built in 1994), AT5 (built in 1998), AT7 (built in 2009), AT8 (built in 2010), and AT9 (built in 2012), with a total of 14,526.26 m2 of built area and 136 classrooms.
Steps (I), (II), and (III) are the processes of construction and feedback of the data related to the object of study and the lighting project. Steps (IV) and (V) refer to the manipulation of the simulation output data and the presentation of reports.
In the construction step (I), the indoor and outdoor environments are modeled with one or several floors, the surface characteristics such as colors and materials are defined, and furniture can be inserted. All of these data are available on internal software catalogs and, if they do not exist, it is possible to create the desired texture based on the type of material and degree of surface reflectance. Furthermore, it is possible to import models built in other parametric programs that interface with DIALux evo, such as ArchiCAD, AutoCAD, Revit, and SketchUp. In the construction stage, the user defines the activities carried out in each environment and the minimum required illuminance parameter, as well as the artificial lighting performance metrics and visual performance benchmarks.
The study object was previously modeled using the ArchiCAD program and then imported to DIALux evo. For the construction phase, the following data were taken into account:
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Dimensions of 6.80 m in width and 9.80 m in length, totaling 66.64 m2;
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Ceiling height of 3.40 m;
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Openings facing north;
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Windows measuring 9.80 m in length and 1.90 m in height, totaling a glazed area of 18.62 m2;
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Building alignment, specifically a longitude of −47.88° and latitude of −21.98°;
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Time zone range, namely UTC/GMT −3 h.
In the lighting step (II), three parameters were defined: artificial lighting systems, scenarios of simulations, and the energy consumption of the lighting systems.
In the first parameter, luminaires, lamps, and daylight control systems were included, and either the online search tool (LUMsearch), available on the program, or imported photometric file formats, such as *.ldt or *.ies, from luminaries and lamp manufacturers, could be used.
From luminaires or lamp data files, the parameters related to luminous flux (lm), luminous efficiency (lm/W), color temperature (K), and Color Rate Index (CRI) were transferred automatically into the luminaire or lighting system. In addition, some adjustments could be performed manually according to the project’s needs.
At this step, the characteristics of the existing lighting system were also inserted and, subsequently, characteristics of potentially more efficient luminaires were considered. The following characteristics were defined:
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Artificial lighting comprising 12 (twelve) luminaires, each with 2 (two) fluorescent lamps of 32 W each and 3.5 W for the ballast, totaling 71 W per luminaire;
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Direct light distribution type;
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Luminaire dimensions of 1.520 m × 0.167 m × 0.076 m each;
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Lighting activation system comprising 3 switches activating the front row of the board, one row in the middle of the room, and two rows at the back;
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Height of 2.35 m between the luminaire and the work surface;
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Common hours of use from 08:00 a.m. to 12:00 p.m., and from 02:00 p.m. to 06:00 p.m., from Monday to Friday for 10 months a year, totaling an average usage of 1760 h per year.
The 22nd of June was considered as a sample, during the winter solstice, of a period with low solar incidence. For this simulation, the least favorable scenario in relation to daylight was considered, because if the natural lighting inside the classroom met the standardized levels proposed by this research in this condition, it would also meet the standardized levels in a scenario with a greater solar incidence, that is, the summer solstice, for example.
In addition, the electric power consumption of artificial lighting was considered the actual usage behavior; this was simulated by manually turning the lights “on” during the common hours of use.
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Maintained Illuminance (Em): On the reference surface of classrooms, the maintained illuminance suggested is not less than 300 lx.
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Uniformity (U): Since it is a classroom and, therefore, students’ arrangements are flexible, the work area considered was the total room area, discounting a range of 0.5 m from the walls, and thus presenting the planned illuminance uniformity of ≥ 0.60 (ratio between the minimum value and the average value).
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Color Aspects: This refers to the qualities of lamp colors, which influence the visual performance and the well-being of users. The appearance of a color can be represented by its correlated color temperature, which can be classified as warm (below 3300 K), intermediate (3300 K to 5300 K), or cool (above 5300 K). The CRI (Color Rate Index) defines the color reproduction, and the quality increases as it approaches the maximum value of 100. Because the object of study is an environment where users remain for long periods, the CRI recommended is greater than or equal to 80.
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Reflectance: All elements inside the classroom were considered (concrete beam: 0.40; walls: 0.81; blackboard: 0.22; door: 0.48; teacher’s desk: 0.15; teacher’s desk structure: 0.31; teacher’s chair: 0.50; student’s chair clipboard: 0.65; student’s chair upholstery: 0.02; student’s chair structure: 0.00; floor: 0.20; roof: 0.40).
Suspended luminaires were maintained, since this type of mounting avoided the need to lower the ceiling, affecting the ventilation and the aesthetics of the classroom. In order to establish the optimum mounting height, three height mountings (2.40 m, 2.60 m, and 2.80 m) were considered.
In addition, two relative luminaire positions were considered. One was at 0°, related to the orientation of the building to the north, and one was at 90°. The two positions were used to verify their influence on the results to find out the best position for the luminaires.
From the characterization of the lighting systems, it was possible to organize the systems in groups and define scenarios using natural lighting, artificial lighting, or the integration of lighting systems by using the “light scenarios” tool available on the DIALux evo software.
For scenarios containing natural lighting, the clear sky model was considered. In addition, scenarios that presented only natural or artificial lighting were simulated. The integration of both lighting systems throughout the classroom’s operating hours was also performed.
In the calculation object (III), parameters related to the lighting calculation were assigned, such as the definition of the use plan and marginal zones, for example. In this case, the use plan considered was 0.75 m from the ground, and the marginal zone was 0.50 m from the ground. Diagrams were also configured, and they represent the illuminance obtained in the model in relation to the plans defined through value graphics, isographic lines, and color scales (in lux or in candela per m2).
Then, after defining these three steps (construction, lighting, and calculation objects), the user selected the calculation option and the program returned the overview of the results obtained on all calculation surfaces.
The export option (IV) step allows users to generate images of their projects from different perspectives and to save them.
The documentation stage (V) presents reports containing the results obtained in the simulation and information about the lighting systems. It can find values for the total number of luminaires, the mounting height of the lighting system and lighting power density (expressed in W/m2 and W/m2/100 lx), luminous performance (in lm/W), and uniformity in the usage plan, for example.
Therefore, from the simulation, it was possible to state the best arrangement for the artificial lighting and natural lighting usage; it was also possible to integrate and suggest improvements for the lighting systems.
5. Conclusions
The results of the DIALux evo simulations demonstrated that the classroom does not receive enough natural lighting throughout the area of the work plan during the day, considering the time of usage, thus requiring supplementation with artificial lighting to maintain a minimum illuminance of 300 lx during hours of use.
The simulation results also pointed out a new optimized artificial lighting arrangement; therefore, the best solution consists of six luminaires, 30 W LED luminaires, positioned at 90 degrees to the north and mounted at a height of 2.80 m, which achieved the required illuminance level inside the classroom.
When considering the replacement (retrofit) of the existing system of luminaires of 71 W (two fluorescent lamps with ballasts) with a new system with 30 W LED luminaires (including driver), the luminous performance of the system increased by 79.5%.
In terms of electric energy consumption, electricity savings of around 58% were obtained when comparing the existing artificial lighting system with the artificial lighting system of LED luminaires.
Annual savings of 64% were achieved when comparing the existing artificial lighting system with the artificial lighting system of LED luminaires integrating daylight usage and the manual control device according to a timetable.
In addition to the complexity of artificial lighting control, implementations to equalize the internal classroom luminance that may require sensors, wiring, and electronic devices may lead to expensive investments, which are often not available in a university’s budget.
On the other hand, the luminaires’ retrofit and new arrangement (positioning to the north) and the proposed manual lighting control may be feasible to implement due to the low cost of the components and services for its implementation, considering that the resources are available as routine maintenance items on universities’ budgets.
Furthermore, the adoption of manual operation by classroom users (teachers and students) may be a result of a pedagogical regard to the need for balanced lighting, as well as a way to increase awareness about the importance of saving energy, which can be expanded beyond the classroom. This is a simple measure that can contribute, even in a small scale, to achieve SDG 7.
It is possible to emphasize that research should consider automatic control lighting systems, based on year-long simulations, incorporating sensors and devices with Wi-Fi communication more persistently to automatic lighting control. In addition, an incorporation of the IoT (Internet of Things) and correlated matters that are currently available on the market can be expected.
Meanwhile, it can be a good practice to encourage research to be conducted on electricity savings by mapping similar classrooms in university buildings, resulting in large scales of annual electricity consumption savings, and of course, promoting less harmful effects to the environment.