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Energy Saving Benefits of Daylighting Combined with Horizontal

Exterior Overhangs in Hot-and-Humid Regions

Speakers:

Huang, Kuo-Tsang 1; Fu, Chun

2

1 Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei,

Taiwan 2

Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei,

Taiwan

Abstract: The exterior shading is an effective way to reduce heat gains through blocking the

radiation. However, it also reduces the daylight availability, causing increased artificial

lighting energy consumption. The purpose of this paper is to investigate the impact of various

overhang design combinations on HVAC and lighting energy use considering visual comfort.

EnergyPlus is used for energy related simulations whilst daylight is simulated by DAYSIM.

The results show that building fenestration orientation and its opening area ratio mainly

influenced the energy consumption and visual comfort. The performance of the optimal design

regarding the exterior setup as well as building fabrics are also discussed.

Keywords: visual comfort, energy efficiency, exterior horizontal overhang, Taguchi method

Introduction

In the subtropical regions, because there is a great amount of solar radiation during the whole

year, which causes high temperatures in indoor environment, more cooling requirement is

needed to reduce thermal discomfort. Thus, the exterior horizontal overhang is an efficient

means to reduce the indoor heat gain via direct solar irradiance. However, the exterior

overhang also prevents the daylight entering into indoors, causing increased artificial lighting

energy consumption. The use of lighting would also contributed to the cooling load and

burdens the cooling energy. Besides, visual comfort, which considers both the degree of

illuminance and the sunlight glare, should be considered when utilizing daylighting design.

There have been many previous studies concerned with efficiency and impact of the

overhang. Ho [1] investigated the optimal design of external shading device in a classroom in

subtropical Taiwan, and the target is to minimize the lighting power cost. Besides, he also

calculated the uniformity of the classroom to evaluate visual comfort. David [2] studied the

effect of different sizing of shading devices and assessed cooling load, artificial lighting, and

visual comfort in tropics. Different types and varying d/h (d: depth of shading; h: window

height) of shading devices are studied. Lim [3] conducted an empirical study for a

government office building and compared the field measurement with the result in simulation

tool Radiance in order to validate the accuracy of Radiance. Second, some modifications of

shading device were simulated to assess the effect on visual comfort, such as Visual Comfort

Probability (VCP), CIE Glare Index and uniformity. However, relatively few research

discussing on the overall impacts on both total energy consumption and the visual comfort

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simultaneously. As exterior shading lowers the heat gain from solar irradiance, the additional

artificial lighting heat discharge due to insufficient illuminace would also increase air

conditioning energy, the interactive behavior between them is necessary to be discussed in

achieving optimal energy balanced passive design. In terms of visual comfort, the available

daylight as well as daylight glare should be considered, and they are easily neglected while

minimizing the total energy consumption.

The aim of this research is to quantify the energy conservation benefits considering visual

comfort and to propose proper exterior shading design parameters in a typical office building

situated in hot-and-humid Taiwan. To this end, we choose two software as our simulation

tools: EnergyPlus and DAYSIM. The former one is a well-known software in simulating

building energy field, which has reliable accuracy in simulation of energy consumption.

Although EnergyPlus is capable in simulating daylight, previous study pointed out that it has

some limitations and as not accurate as Radiance [4]. DAYSIM is developed based on

Radiance’s algorithms and have been well developed and widely validated. It is proved as a

reliable software in simulating daylight [5]. Therefore, EnergyPlus coupled with DAYSIM

were adopted in this study.

Methodology

As shown in Fig. 1, research consists three major parts, (1) choosing factors and design of

experiments, (2) daylight and energy consumption simulation, and (3) the analysis of the

impact. To understand the impact of shading designs on energy use and visual environment

under different design setup, the properties of horizontal overhang has been studied. The

orientation of the building, glazing types, and Window to Wall Ratio (WWR) are also

considered. Lighting and HVAC energy consumption were assessed by simulation, and the

relationship between them is also discussed. Useful Daylight Index (UDI) and Daylight Glare

Probability (DGP) were used to evaluate the daylight availability and daylight glare

respectively. In DAYSIM simulation, since generating annual illuminance data and DGP in

each possible simulation run is time-consuming, the exploration of all the parameter

combinations is impractical. Therefore, design of experiment technique (Taguchi method)

was used to arrange the experiments efficiently for reducing large number of simulation runs.

Geometry and parameters of the model

The reference model in this study is a typical office room located in hot-and-humid Taiwan.

The office room is a middle floor of a multi-story building, which ceiling height is 3.5 m, 11.5

m in width and 6.3 m in depth, the total floor area is 72.45 m2 as shown in Fig. 2. The room

has a window on the daylit side, whose width is identical to the width of the room. The wall

with the window is the only exterior wall that exposed to outdoor air, and the coefficient of

thermal conductivity (U-value) is 2.77 W/m2K. All the interior walls in the model are

assumed to be adiabatic as the adjacent spaces are of the same air-conditioning conditions.

The office occupancy on weekdays is defined as 0.2 person per square meter, and it decreases

to 0.1 at the lunch time (12:00-13:00) and near the evening (17:00-18:00). The equipment

power density is 10 W/m2, and the schedule of equipment is the same as the occupancy

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schedule. The lighting schedule was determined based on the averaged indoor illuminance

results simulated from DAYSIM. Parameters and their levels used in orthogonal array

experiment are listed in table 1.

Figure 1. The research scheme

Figure 2. The geometry of the office model and the task area

Table 1. Levels of all factors (including geometry and material properties)

Orientation Depth Position

ratio

Reflectivity Tilt

angle

WWR Glazing

Level 1 (8 levels:

N,NE,E,SE,

S,SW,W,NW)

1.0 m 1.00 70% 90° 70% Single Clear

Level 2 0.7 m 0.75 50% 70° 50% Single Tint

Level 3 0.5 m 0.50 30% 45° 30% Dbl. Low-E

Four design factors of the exterior horizontal overhang shading are discussed, which are

overhang’s depth, vertical relative position to window of the overhang, tilt angle, and the

shading’s material surface reflectivity. The vertical relative position of overhang is defined as

Position Ratio, which is calculated as the ratio of the overhang’s vertical distance from the sill

to the window’s height. Position ratio of 0.5 means the overhang is located in the middle of

windows height. Tilt angle is defined as the angle between the shading plate and the wall. An

angle of 90 degrees means the overhang is installed perpendicular to the wall. Three types of

overhang materials with various colors and surface reflectance are chosen.

6.3

m

11.5 m

1.5

m

1.0 m

Task area

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Design of Experiments

To carry out all the possible parameter combinations listed in table 1 is time-consuming and

the number of total simulation runs would be as high as 5832 times which is deemed

impractical. Therefore, Taguchi method, which is an efficient statistical design of

experiments, has been applied to minimize the runs of experiment. Taguchi orthogonal design

table of L27 (313) was chosen in this case. However, the factor of orientation has 8 levels

while there are only three-level columns in L27. To resolve this problem, column-merging

method was adopted to combine 4 columns into 1 column to accommodate the orientation

parameter.

The Assessment of Daylight Availability and Visual Comfort

Daylighting was simulated via DAYSIM. The reflectance of ceiling, wall and floor are 0.8,

0.5 and 0.3 respectively. The height of the work plane of interest is 0.85 m above floor. The

task area of the office is 9.5 m wide, 4.8 m deep (see Fig. 2). This task area is near the

window, and the view direction of the occupant which is used in calculating DGP is

perpendicularly vectored outward to the window. Useful Daylight Index (UDI), which is used

to quantify useful daylight illuminance within the range of 300-2000 lx is used to characterize

the illuminance quality by daylight. Daylight Glare Probability (DGP) is an index to represent

the probability of experiencing discomfort glare. There are four categories of the DGP values:

intolerable (DGP>0.45), disturbing (0.45>DGP>0.4), perceptible (0.4>DGP>0.35), and

imperceptible (DGP<0.35). The percentage of annual work hours in which the DGP is less

than 0.4 is considered acceptable herein the study. The minimum illuminance level of 500 lx

suggested in ISO 8995 for task area is used as minimum requirement of task illuminance. The

lighting switch on/off schedule used in EnergyPlus energy simulation is determined by

illuminance map output from DAYSIM simulation. The artificial light is switched on with

dimming control to maintain the illuminance level in all the task area above 500 lx. This

lighting schedule was afterwards fed to EnergyPlus to simulate the annual lighting energy.

Energy Consumption Analysis

Fig. 3 indicates the mean effects (compared to the mean value of total 27 experiments) of

factors on energy consumption, and the energy use is assessed with Energy Use Intensity

(EUI) index. Both HVAC and lighting energy consumption is influenced mainly by

orientation, WWR, and glazing types. Overall, south-facing and north-facing cases save more

total energy than other directions. The influence of WWR is very different between HVAC

and lighting. WWR of 30% causes a relatively high mean effect of 3.6 kwh/m2.yr because the

lighting demand is much higher than the average value. In contrast, WWR of 70% may save

more energy for the total mean effect is -0.4 kwh/m2.yr. Double low-E glass may lead to

higher lighting energy while single clear glass has higher HVAC energy.

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Figure 3. The mean effects of all factors on EUI (including HVAC and lighting)

Visual Comfort Analysis

As shown in Fig. 4, there are more available daylight in the directions of N, NW, and NE.

Among all factors, WWR have the greatest influence on the value of UDI. WWR of 70% have

a mean effect of -11.6 %, while 30%WWR have a mean effect of 8.8%. Although the cases

with high WWR or in the directions such as east or west have more sunlight from the window,

the illuminance level is too high and would cause less useful daylight. The reflectivity of 0.7

has a mean effect of -2.9 % suggesting that higher reflectivity seems to have negative effect

on daylight utilization. Similarly, the directions of N, NW, NE, and lower WWR would have

more times in which DGP is acceptable for people (DGP<0.4). Because the experiment no. 7

(WWR of 30% with tilt angle of 45 degrees) has scarce daylight, the UDI is as low as 32.6 %

resulting no glare occurred in all year. This situation greatly influence the mean effect of east-

direction, so there is an obvious difference of mean effect between east and west.

Figure 4. The mean effects of all factors on visual comfort (including UDI and DGP)

The Optimal Design

To study the relationship between energy consumption and daylighting, the 27 main effects of

total energy, UDI, and percentage of DGP<0.4 are plotted in Fig. 5. From these two figures, it

reveals that data points concentrate in the bottom-right and top-left parts. This tendency

indicates that the pursuit of high energy saving may possibly decrease visual comfort. To

determine the optimal solution, the average lines are drawn to divide the graph into four parts.

The bottom-left part means low visual comfort (including UDI and DGP) and high energy

consumption, which is the worst among all parts. The bottom-right and top-left, in which most

plots fall, are either having low visual comfort or high energy consumption. The best part,

which is the top-right with good visual comfort and low energy consumption, have total 3

points in this area. These 3 points are experiment no. 2, 10 and 23. Although they are not the

best choice with respect to visual comfort or energy saving individually. It’s quite difficult to

achieve maximum energy saving and the best visual comfort simultaneously, balanced

compromise should be considered.

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Figure 5. Plots of the total energy consumption and visual comfort

Among the experiment no.2, 10 and 23, no. 2 has better energy saving and visual comfort,

thus it is discussed in Fig. 6 and 7, which is a false-color map where the X-axis is the day of

year and the Y-axis is the time of day, is used to present annual variations of UDI and DGP.

Another facing north case, experiment no. 1, with worse visual comfort is also presented as a

contrast. It is obvious that no. 2 have less daylighting glare during the whole year. However,

no. 2 still has 14.2% of total work hours in which people experience unacceptable glare. It is

noticeable that useful daylight of no. 1 is much less than no. 2 near the noon. This situation

can be compared to the figure of DGP, as Fig. 7, it is found that the time of experiencing more

daylighting glare also accompanied with decreased amount of useful daylight. The main

reason for visual discomfort in experiment no. 1 is the high WWR of 70%, which caused

much more over illuminating areas indoors, while WWR of no. 2 is just 50%.

Figure 6. Temporal map of UDI for experiment no. 1 and no. 2

Figure 7. Temporal map of DGP for experiment no. 1 and no. 2

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Conclusion

In this study, several horizontal overhang shading designs were investigated in subtropical

Taiwan, in terms of both energy consumption and visual comfort. From the analysis of the

simulation results, several conclusions can be obtained as follows:

1. South and north directions are beneficial for energy saving, but north-facing cases have

better visual comfort.

2. WWR greatly influences both energy consumption and visual comfort. Although WWR of

30% has more lighting demand, it still has better energy saving (due to less HVAC energy)

and visual comfort than WWR of 50 and 70%.

3. Among 27 experiments, no one achieves minimum energy consumption and best visual

comfort at the same time. However, it is possible to strike a balance between them and

having remarkable effect.

Reference

[1] Ho, M.-C., et al. (2008). Optimal sun-shading design for enhanced daylight illumination

of subtropical classrooms. Energy and Buildings, 40(10): p. 1844-1855.

[2] David, M., et al. (2011). Assessment of the thermal and visual efficiency of solar shades.

Building and Environment, 46(7): p. 1489-1496.

[3] Lim, Y.W., et al. (2012). Building façade design for daylighting quality in typical

government office building. Building and Environment, 57: p. 194-204.

[4] Ramos, G. and E. Ghisi. (2010). Analysis of daylight calculated using the EnergyPlus

programme. Renewable and Sustainable Energy Reviews, 14(7): p. 1948-1958.

[5] Reinhart, C. and B. Pierre-Felix. (2009). Experimental validation of autodesk® 3ds max®

design 2009 and daysim 3.0. LEUKOS - Journal of Illuminating Engineering Society of

North America, 6(1): p. 7-35.