A Parametric Approach for Achieving Daylighting ...

PLEA 2016 Los Angeles - 32th International Conference on Passive and Low Energy Architecture. Cities, Buildings, People: Towards Regenerative Environments

A Parametric Approach for Achieving Daylighting Adequacy and Energy Efficiency by Using Solar Screens AYMAN WAGDY1, FATMA FATHY2 1

Creative Industries Faculty, Queensland University of Technology (QUT), Brisbane, Australia 2 Faculty of Engineering, Ainshams, Cairo, Egypt

ABSTRACT: Daylighting decreases the energy needs of electric lights; however, in desert climates, it may cause excessive heat gains and thereby increase cooling loads. In this paper, we implemented a parametric approach to tackling this problem through the use of a solar screen in order to find its optimum configuration in terms of three parameters: window-to-wall ratio (WWR), louver tilt angle, and depth ratio. Based on the predefined range of each parameter, we simulated 100 cases for a generic south-oriented classroom and evaluated their daylighting and energy performance. Results have demonstrated the convergence of solutions at high WWRs, with high depth ratios and downward tilt angles. In addition, a correlation between two different daylight metrics and energy loads was detected. Keywords: Energy, Daylighting, Overlit, Parametric, Solar Screens, Exhaustive Search

INTRODUCTION Daylighting design is a challenging process in which conflicting objectives must be resolved. Thus, it is essential to consider both qualitative and quantitative aspects in order to reach high performance criteria, especially when aiming to achieve a balance between daylighting adequacy and energy efficiency. These are two important design aspects that may be contradicting in desert climates due to a high level of solar radiation that could increase cooling loads. Therefore, a shading device such as a solar screen could be the solution. Solar screens have proven their efficiency in hot-arid environments. They have been widely used in the Middle East for their shading capacity and to resolve privacy issues. A trend of their use in contemporary buildings has started to evolve, with the role of these screens shifting from one of aesthetics to one of environmental performance. A study has investigated different perforation ratios on both energy savings and daylighting autonomy through an experimental simulation. The results revealed that perforations ranging from 30% to 50% are the best for reaching a compromise between thermal and daylighting requirements (Batool & M.K. Elzeyadi, 2014). Another study addressed the perforation percentages and depth ratios of fixed solar screens for a residential space in a desert climate. For the south orientation, 30% less energy consumption was reached, with a perforation percentage of 90% and a depth ratio of 1 (A. Sherif et al., 2012). In addition to perforation percentages, rotation angle and aspect ratio are two effective screen parameters of daylighting performance and solar radiation transmittance. This was investigated in a study consisting of three consecutive phases: daylight availability, annual daylight glare probability, and

annual solar energy transmittance. It was concluded that the effectiveness of screens with horizontal aspect ratios outperformed those with vertical ones; 12:1 and 18:1 (H:V) were recommended for the north, 12:1 for the east, and 3:1 for the south orientation (A. H. Sherif et al., 2012). In an attempt to find balance among daylighting performance, thermal comfort, and cooling energy consumption in hot-humid climates, a parametric study investigated the influence of 12 variations in window size and overhang depth in a southwest-oriented classroom (Supansomboon & Sharples, 2014). In another attempt to find the trade-off between daylighting performance and energy savings, Gadelhak et al. (2013) investigated different configurations of horizontal sun breakers. Consequently, the effects of their number, depth, and inclination angle were studied for a southoriented office space. Results have reached satisfactory balance for both daylighting and energy needs; a daylit area of 72% and a reduction of 34% in energy loads were reached. As for optimization approaches, a study was carried out to identify the optimum nonconventional solar screen configurations based on energy performance through Particle Swarm Optimization (PSO). A generic optimization program called GenOpt was used with the Energy Plus simulation program, where a hybrid algorithm was utilized: PSO coupled with a Generalized Pattern Search algorithm (GPS). The former was used for global search, while the latter performed the local search in order to refine the results. The results in the south orientation showed the significance of the horizontal depth bars on energy savings, where the highest energy savings reached 30.7% (Arafa et al., 2013).

PLEA2016 Los Angeles - Cities, Buildings, People: Towards Regenerative Environments, 11-13 July, 2016

The literature confirmed that substantial efforts were taken to encourage the integration of daylighting and energy strategies. In this paper, we sought to apply this integration in educational spaces with the objective of enhancing the learning environment, as various studies have proven the correlation between student/staff performance and daylighting adequacy inside the classroom (Edwards & Torcellini, 2002), (Heath & Mendell, 2002), and (Nicklas & Bailey, 1996). The aim of this paper is to develop a framework to conduct solar screen optimization for both daylighting and energy requirements in hot-arid climates like Cairo, Egypt. In the case study, the focus on classrooms stemmed from our objective of enhancing the learning environment that could have a significant impact on both student and staff performance. METHODOLOGY We conducted integrated daylighting and thermal simulations by using Diva-for-Rhino, a plug-in for Rhinoceros modelling software that interfaces Radiance, Daysim, and Energy Plus software (Solemma, 2014). We sped up the simulation process by executing it in parallel with SpeedSim-for-DIVA (Wagdy, 2015). In terms of the solar screen modelling, we used the Rhinoceros parametric tool Grasshopper (Rutten, 2014). Daylighting and Energy Evaluation Criteria Daylighting analysis was performed with Illuminating Engineering Society (IES) metrics, Spatial Daylight Autonomy (sDA300/50%), Annual Sunlight Exposure (ASE1000/250hr), and Daylight Availability examined in the first phase; then, total energy loads were calculated in terms of lighting, cooling, and heating loads. First, sDA300/50% denotes the percentage of the space area that receives 300lux or more for at least 50% of the occupied hours. Diffuse daylight is favoured while direct sunlight is a source of visual discomfort, so the evaluation of sDA is accompanied by another metric. This metric is ASE1000/250hr, and it indicates the space area that receives direct sunlight at a value greater than 1000lux for more than 250 hours. The IES recommended that sDA should be at least 75% of the space area, while ASE should be less than 3%. We used another metric for daylighting as well: Daylight Availability (DA) (Reinhart & Wienold, 2011), which divides the space area into three zones and gives a warning when daylight exceeds the maximum threshold for more than 5% of occupied hours. This area signifies the ‘overlit’ area, and we intended to show its correlation with energy loads (especially the cooling ones). In general, DA has the same minimum threshold as daylight autonomy, although it involves adding a maximum threshold that is ten times the minimum. The other two zones are ‘partially daylit’ where the minimum threshold (300lux) is received less than 50% of the occupied times. The ‘daylit’ area denotes the area

receiving illuminance levels between 300 and 3000lux at least 50% of the time. In this paper, we intended to find the applicability of reaching optimum solutions of 100% sDA and 0% ASE while preserving the lowest possible energy loads. In addition to finding the optimum set of solutions, we examined the general tendency of each parameter and the interactions among them to determine how they affected the two main criteria. Table 1: Room and Screen Parameters SPACE PARAMETERS Floor Level 2nd floor (+6.00) Room Area 38.5m2 Floor Height 3.0m INTERNAL SURFACES REFLECTANCE Ceiling 80% Walls 50% Floor 20% Furniture 50% WINDOW PARAMETERS Window-to-Wall Ratio 20% to 60%, with an increment (WWR) of 10% Glazing Double Clear Pane (VT=80%) Window Frame Metal Diffuse SCREEN PARAMETERS Screen Louver Count (C) 5 Screen Louver Depth Ranges from 75% to 150%with Ratio (D) (of the screen an increment 25% height (H)) Screen Louver Angle (A) Ranges from -20° to 20°, with an increment of 10° (0° is the horizontal state) Screen Reflectance 80%

Figure 1: Classroom Configurations

Classroom Configurations We selected a generic south-oriented classroom space to conduct this simulation experiment. The space was located in Cairo, Egypt (30°6'N, 31°24'E, alt.75m), with no external obstructions. The space parameters and screen configurations are shown in Figure 1 and Table 1.


In addition, the space was divided into a square grid (0.3m*0.3m), forming 414 analysis points on the working plane at 0.9m above the ground. Screen Modelling The solar screen was varied in terms of three main parameters: 1) Window-to-wall ratio (WWR); the screen covered the whole window area, which ranged from 20% to 60% of the wall area. 2) Screen depth ratio (D); it ranged from 0.75 to 1.5 with an increment of 0.25. Dividing the screen depth (Ds) by the screen height (Hs) helped to define the depth ratio. Hs, as shown in Figure 2, referred to window height, which changed according to WWR and louver count, the latter of which was fixed at five. Accordingly, Ds was dependent on Hs to maintain the predefined four depth ratios. 3) Louver angle (A); figures ranged from -20° to 20° with an increment of 10°.

Figure 1 screen modelling (Wagdy & Fathy, 2015) Simulation Process The previous set of ranges combined and formed 100 different alternatives in a single exhaustive list (Curtin, 2007), (Daintith & Wright, 2008), and (Wagdy & Fathy, 2015). The simulation process was automated using parallel algorithms to speed up the radiance simulation time. Radiance parameters were set according to the IES recommended values (IESNA, 2012) as shown in Table 2. Table 1: Radiance Parameters Evaluation Metrics sDA ASE

Ambient Bounces 6 0

Ambient Divisions 1000 1000

Direct Threshold 0 0

SIMULATION RESULTS AND DISCUSSION The objective of this paper is to find the screen configurations that achieved optimum daylighting performance (100% sDA and 0% ASE), and then to find the least possible total energy loads. A compromise between daylighting requirements and energy loads was considered. First, daylighting results were analysed. It was shown that daylighting performance regarding sDA and

ASE was significantly affected by all three parameters: WWR, depth ratio, and tilt angle. In general, both sDA and ASE increased in line with WWR. This increase was amplified by upward tilt angles and low depth ratios. Thus, a conflict between sDA and ASE occurred, as decreasing ASE requires increasing sDA. By exploring and analysing all sets of parameter ranges, we found a range of values that achieved optimum daylighting performance in terms of sDA and ASE as shown in Table 3. In Table 4, graphs (4a) and (4b) represented low depth ratios of 0.75 and 1, respectively. In the former, sDA stabilized at its peak starting from 30% WWR, while at 20% WWR it decreased from 94% to 79%; the tilt angle decreased from 20° (upwards) to -20° (downwards). Similarly, ASE decreased as a result of the tilt angle; however, it was still high where the lowest achievable ASE was 3% at 20% WWR and a tilt angle of -20°. In the latter, at depth ratio 1, sDA maintained its maximum value at high WWRs starting from 40%, whereas at 30% WWR it stabilized at 99% and decreased by only 1%. At 20% WWR, sDA decreased from 90% to 63% as the tilt angle decreased from 20° to -20°. ASE also decreased according to the downward tilt angles. However, the decrease at this depth ratio superseded that of 0.75, as it was able to reach lower ASE values. For instance, at 40% WWR, ASE decreased from 34% at 20° to 1% at -20°. The effect of decreasing tilt angles was magnified by increasing depth ratios. Therefore, at depth ratio 1 and downward tilt angles, a balance between high sDA and low ASE values was achieved. However, it still could not reach the required optimum (100% sDA and 0% ASE). At depth ratios 1.25 and 1.5 as shown in graphs (4c) and (4d), there was a significant decrease in sDA at low WWRs (20% and 30%) that was magnified by downward tilt angles. For instance, at 20% WWR, sDA decreased from 83% to 49%, and from 76% to 40% at depth ratios 1.25 and 1.5, respectively. In turn, these large depth ratios did not influence WWRs starting from 40%, as sDA was constant at 100% for all tilt angles. The advantage of these large depth ratios with the aid of downward tilt angles (-10°, -20°) appeared in the fast convergence of ASE. It was able to reach its bottom line at all WWRs. Thus, optimum cases (100% sDA and 0% ASE) were achieved at WWR 40%, WWR 50%, and WWR 60% at tilt angles -20° and -10°, as shown in Table 3. Second, the effects of all screen parameters on total energy loads regarding heating, cooling, and electric loads were analysed as shown in Table 5. By comparing the four graphs that represented the four depth ratios, it was obvious that total energy loads decreased as depth ratio increased. At small WWRs, electric loads increased to maintain the required illuminance levels, yet the small glazing area in reducing the cooling loads compensated for this increase. Thus, total energy loads decreased in


line with the decrease in WWR. However, this was not always the case, as decreasing tilt angles (downwards) and increasing depth ratios caused a substantial increase in electric loads, and hence, an increase in total loads at small WWRs relative to large WWRs. For instance, in Graph (5d) at depth ratio 1.5 and a -20° tilt angle, 20% of the WWR reached 185 KWH/m2, which is the highest total energy due to the increase in electric load that reached 19 KWH/m2 (while 60% WWR was lowered by 3 KWH/m2). It reached 182 KWH/m2, the second-highest total energy, with electric energy contributing only 2 KWH/m2. In the same Graph (5d), at 40% WWR, it reached 172 KWH/m2, which was the lowest total energy load not only relative to other WWRs, but of all 100 cases. Table 3 showed the best optimum alternatives from the daylighting perspective and their corresponding total energy loads. It was found that the case with the least total energy load was the best at daylighting, reaching 100% sDA and 0% ASE. Thus, this configuration of 40% WWR, -20° tilt angle, and 1.5 depth ratio proved its superiority in balancing the daylighting and energy load requirements.

0% ASE; however, the DA posed a new dimension for energy efficiency, along with daylighting adequacy. In short, daylighting performance was explored in terms of the IES approved method. Reaching the required illuminance levels while decreasing direct sunlight penetration was achieved by way of downward tilt angles and high depth ratios of 1.25 and 1.5, where the optimum results were realized at WWRs starting from 40% as illustrated in Table 3. Additionally, energy loads needed to be minimized in order to balance daylighting adequacy and energy efficiency. Regarding heating loads, they were more or less constant at a specific level. They ranged from 12 KWH/m2 at high WWRs (60%) to 9 KWH/m2 at low WWRs (20%). In contrast, cooling loads were the most influential in desert climates, ranging from 154 KWH/m2 to 240 KWH/m2. It was found to be more closely related to the ‘overlit’ area rather than the annual sunlight exposure as an indicator of heat gain. Besides, the impact of electric loads at low WWRs and downward tilt angles was considerable. Since its increase neutralized the decrease in the cooling loads, it caused an increase in total energy loads.

Table 3: Optimum cases from the perspective of daylighting and their corresponding energy loads

CONCLUSION Evaluating all possible parametric combinations resulted in a clear picture of the effects of each parameter and their role in achieving the intended outcome. In this paper, the objective was to balance daylighting adequacy and energy efficiency. The results highlighted the beneficial outcomes of using solar screens in classrooms in terms of both criteria. In particular, the solar screen proved its effectiveness in reaching the optimum daylighting performance (100% sDA, 0% ASE) while maintaining low energy loads. This was achieved with the lowest total energy loads at 40% WWR, a downward tilt angle of -20°, and a 1.5 depth ratio.

The other cases shown in Table 3 have the same optimum constant value of 100% sDA and 0% ASE, whereas energy loads vary from 197 KWH/m2 to 172 KWH/m2. This variation was required to be correlated with a daylighting value and give an indication about energy performance, in addition to giving a priority to one solution over another when selecting between them. Thus, Daylight Availability (DA) was calculated to determine the relationship between the ‘overlit’ area and energy loads. It was found that the increase in total energy appeared with an increasing ‘overlit’ area. These cases have no significant direct sunlight, as they have

The correlation between the ‘overlit’ area and increasing energy loads was deduced, despite their having the same ASE (0%) as shown in Table 3. Therefore, calculating Daylight Availability provided an indication about energy loads, and hence, daylighting optimum cases can be sorted in terms of energy without being calculated. These results are limited to the conditions of the case study; however, the same methodology paves the way for further exploration of the impact of this solar screen on other orientations. Ideally, this will allow the solar screen to reach more sustainable classroom designs in desert climates.

PLEA 2016 Los Angeles - 32th International Conference on Passive and Low Energy Architecture. Cities, Buildings, People: Towards Regenerative Environments

Table 4: sDA and ASE for all depth ratios showing their performance at all WWRs and tilt angles (100 cases) DEPTH RATIO 0.75 (4A) DEPTH RATIO 1 (4B)

-20

-10

60% 50% 40% 30% 20%

0 Tilt Angle (°)

10

-20

20

sDA& ASE (%)

-20

-10

0 Tilt Angle (°)

60% 50% 40% 30% 20% 10

20

DEPTH RATIO 1.5 (4D)

60% 50% 40% 30% 20%

0

-10

10

100 90 80 70 60 50 40 30 20 10 0

sDA & ASE (%)

DEPTH RATIO 1.25 (4C) 100 90 80 70 60 50 40 30 20 10 0

100 90 80 70 60 50 40 30 20 10 0

sDA& ASE (%)

sDA& ASE (%)

100 90 80 70 60 50 40 30 20 10 0

20

-20

-10

Tilt Angle (°)

sDA

0 Tilt Angle (°)

10

60% 50% 40% 30% 20% 20

ASE

260 250 240 230 220 210 200 190 180 170

60% 50% 20% 40% -2030%

-10

0 Tilt Angle (°)

260 250 240 230 220 210 200 190 180 170

60%

50% 40% 30% 20% 10

DEPTH RATIO 1.25 (5C)

20

Total Energy [KWH/m2]


Table 5: Total energy loads for all depth ratios, showing energy performance at all WWRs and tilt angles (100 cases) DEPTH RATIO 0.75 (5A) DEPTH RATIO 1 (5B)

60% 50% 20% 40% -2030%

-10

0 Tilt Angle (°)

60% 50% 40% 30% 20%

10

DEPTH RATIO 1.5 (5D)

20

260

260

250

250

240

240




230 220 60% 50% 40% 20% 30%

210 200

60% 50% 20% 30% 40% -20

190 180 170 -10

WWR

0 Tilt Angle (°)

60%

10

20

50%

REFERENCES Arafa, R., et al. (2013). Energy Efficient Configuration of NonConventional Solar Screens Using Hybrid Optimization Algorithm Optimizing Screen Depth, Perforation and Aspect Ratio. Paper presented at the Proceedings: BESS-SB13, Building Enclosure Sustainability Symposium Sustainable Buildings Conference, Advancing Toward Net Zero. Batool, A., & M.K. Elzeyadi, I. (2014). From Romance to Performance: Assessing the Impacts of Jali Screens on Energy Savings and Daylighting Quality of Office Buildings in Lahore, Pakistan. Paper presented at the 30th international PLEA conference, Ahmedabad, Indea. Curtin, M. (2007). Brute Force. Dordrecht: Springer. Daintith, J., & Wright, E. (2008). A dictionary of computing: Oxford University Press, Inc. Edwards, L., & Torcellini, P. A. (2002). A literature review of the effects of natural light on building occupants: National Renewable Energy Laboratory Golden, CO. Gadelhak, M., et al. (2013). High Performance Facades: The Effect of Sun Breakers on Daylighting Performance and Energy Consumption in South Oriented Office Spaces. Paper presented at the AEI 2013@ sBuilding Solutions for Architectural Engineering. Heath, G., & Mendell, M. J. (2002). Do indoor environments in schools influence student performance? A review of the literature. Paper presented at the Proceedings of the 9th International Conference on Indoor Air Quality and Climate, Indoor Air 2002. IESNA, I. (2012). LM-83-12. IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). New York, NY, USA, IESNA Lighting Measurement. Nicklas, M. H., & Bailey, G. B. (1996). Analysis of the performance of students in daylit schools. Paper presented at the Proceedings of the National Passive Solar Conference. Reinhart, C. F., & Wienold, J. (2011). The daylighting dashboard–a simulation-based design analysis for daylit spaces. Building and Environment, 46(2), 386-396.

230 220 60% 50% 40% 20% 30%

210 200 20% 60% 50% 30% -2040%

40%

190 180 170 -10

0 Tilt Angle (°)

30%

10

20

20%

Rutten, D. (2014). Grasshopper-Algorithmic modeling for Rhino software version 0.9077. URL http://www.grasshopper3d.com last accessed; . Retrieved 1010, 2014 Sherif, A., et al. (2012). External perforated window Solar Screens: The effect of screen depth and perforation ratio on energy performance in extreme desert environments. Energy and buildings, 52, 1-10. doi: 10.1016/j.enbuild.2012.05.025 Sherif, A. H., et al. (2012). The impact of changing solar screen rotation angle and its opening aspect ratios on Daylight Availability in residential desert buildings. Solar Energy, 86(11), 3353-3363. doi: 10.1016/j.solener.2012.09.006 Solemma, L. (2014). DIVA-for-Rhino software version 3.0: URL http://www.diva4rhino.com ‘last accessed. Supansomboon, S., & Sharples, S. (2014). The Impact of Tropical Classroom Facade Design Alternatives on Daylight Levels and Cooling Energy Consumption. Paper presented at the 30th international PLEA conference, Ahmedabad, Indea. Wagdy, A. (2015). SpeedSim-for-DIVA software version 1.2. Retrieved 12 September, 2015, from

http://www.aymanwagdy.com/#!speedsim/cjg9 Wagdy, A., & Fathy, F. (2015). A parametric approach for achieving optimum daylighting performance through solar screens in desert climates. Journal of Building Engineering, 3, 155-170. doi: http://dx.doi.org/10.1016/j.jobe.2015.07.007