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Abstract - While schools and universities are shaping the
world of tomorrow, they are focused more and more on
sustainability matter. In order to ensure a sustainable future,
the knowledge potential, technology and tools should be come
to use on campuses. An initiative, financed by the Innovation
and Quality Fund of Baden-Württemberg, supports the
development of universities as living laboratories to
demonstrate and implement sustainability. The University of
Applied Sciences Stuttgart aims to become a CO2 neutral
university in cooperation with regional institutions.
This study focuses on minimizing lighting energy
consumption while maintaining visual comfort conditions in
university buildings, with a particular emphasis on two
different types of rooms such as classroom and computer
laboratory. The work is structured in four steps: First light
environment and glare are investigated (daylight and artificial
light) via measurements and simulations. The second steps
deals with the occupancy profiles and the lighting electricity
consumption. The User Satisfaction Questionnaire, which is
based IEA-SHC-Task 50 Subtask D3, is followed by the third
step. As the last step, the best lighting retrofitting options were
identified.
Keywords - artificial light, daylight, glare, lighting energy,
simulation, university, visual comfort
I. INTRODUCTION
NERGY demand reduction and the increased use of
renewables are the main drivers of sustainability. In
educational facilities, the energy demand is an important
issue related to its long occupancy period and user
behaviour. About 30 % of the total energy demand of
universities is required for artificial lighting. Using lighting
control technologies, enhancing daylight and installing
efficient lamps and luminaries can help to reduce
significantly the annual energy demand.
A future-oriented energy supply can only be achieved if
technical potential and existing knowledge are implemented
in the society. An important step forward to a sustainable
future is the development of living labs in universities in
Germany, especially in Baden-Württemberg. The ambition
is to realize individual sustainability targets in cooperation
with regional institutions. The University of Applied
Sciences Stuttgart aims to become a climate-neutral
university. According to the Ministry of Higher Education,
Research and the Arts an important objective is to establish
sustainability as an issue in sciences. Currently nationwide
living labs are founded to support concept and encourage the
interaction. On the one hand they should lead to a better
understanding of social change processes, how to shape
them and to measure their impact. On the other hand living
labs are used as linkage and cooperation structures between
universities and non-university research institutes as well as
between economy, politics, administration and civil society
actors [1]. As one of ten living labs concepts the Stuttgart
University of Applied Sciences is financially supported with
one million euro by the Innovation and Quality Fund of
Baden-Württemberg to become a CO2 neutral university in
cooperation with regional institutions.
Recent studies have estimated that European schools
contribute 15 % of the public sector carbon footprint. It is
assumed that approximately 20-30 % of energy use comes
from artificial lighting in educational buildings. Therefore, it
is a relevant research topic and many research works are
going on this subject. The primary energy consumption of
schools in Luxemburg has increased due to the higher
electricity consumption [2]. The trend of increasing
electricity use in Scottish schools was discussed by Dobson
and Cater [3]. Approximately 46 % of total electricity
demand of the American educational buildings is caused by
office equipment and lighting [4]. Most of these studies try
to detect the efficiencies and potential improvements which
provide energy use reduction.
Glare is a common problem in classrooms and lecture
halls. It occurs when one part of the visual scene is much
brighter than the overall brightness of the rest of the field of
view. Glare can be divided into two types: disability glare is
a decrease in a visual performance due to light scatters
within the eye; discomfort glare refers even without
detrimental to vision the feeling of discomfort. Although,
the glare issue has been studied over a long period, there are
still many unresolved questions. One common finding is that
people perceive a bright surface as disturbing. It is also
believed that some glare can be tolerated if the work place
contains a view to the outside [5-7].
The natural illumination of interior rooms is an important
criterion for providing comfort for its users. Natural light
stimulates the circulation and promotes general wellbeing;
human performance ability is decisively influenced.
Wellbeing can be increased by visual contact to the outside
which informs the user about his environment and the
weather. Such an effect cannot be brought about by artificial
lighting. For that reason, a careful daylighting conception is
needed for a well-functioning building. Furthermore, to
meet the requirements of the occupants, user surveys should
be done to be able to carry out the optimization potential.
An efficient retrofitting approach for improving
lighting solutions: A case study at Stuttgart
University of Applied Sciences
Amando Reber, Dilay Kesten Erhart
Centre of Applied Research on Sustainable Energy Technology, Stuttgart University of Applied Sciences, Stuttgart, Germany,
| 21ream1bke@hft-stuttgart.de | dilay.kesten@hft-stuttgart.de
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II. METHOD
IM of the study is to minimize lighting energy
consumption while maintaining visual comfort
conditions. Fig. 1 shows the two rooms which have been
analysed: a classroom and a computer laboratory. The
principal focus lays on the analysis of light environment to
provide a basis for the development of retrofitting
alternatives.
Fig. 1. Classroom [left] and computer laboratory [right].
A series of lighting measurements were conducted in
order to determine daylight, electrical lighting illuminance
levels in selected spaces. Besides, the total luminance
distribution and the glare potential were investigated. These
collected measurements were compared with the simulation
results. The occupancy schedules were taken and the
electricity consumption was recorded. Since the occupants
are the main focus of the comfort, a user satisfaction
questionnaire was handed out to gather the impression. As a
final point, possible lighting retrofitting alternatives are
developed.
Evaluations of the lighting performance are based on
measurements and monitoring. Illuminance is recorded by
lux-meters across whole room area. In order to monitor the
visual environment, a high dynamic range (HDR) luminance
camera is used in combination with a fisheye lens.
Afterwards, the glare caused by daylight and artificial light
is evaluated. Daylight glare is assessed by the Daylight
Glare Probability (DGP) and artificial lighting glare is
assessed by the Unified Glare Rating (UGR) method. For
the present study, several computer simulation tools are used
to model the daylighting performance of the lecture room
and the computer lab and results are compared with the
measurements. With the purpose of the user-specific
optimization solutions, occupancy profiles and user
behaviour are analysed. The goal is saving energy while
ensuring optimal visual comfort. Possible measures for this
goal include replacing the luminaries and using dimmable
LED lamps, the use of daylight- and presence controlled
lighting systems and the installation of glare protection and
light-diffusing solar shading devices.
A. Room description
The test space is a classroom and a computer lab in
Building 3 of the University of Applied Sciences Stuttgart,
Germany, geographically located at 48°68'N latitude and
9°22'E longitude. Stuttgart has a moderate climate with the
average temperature of the coldest month at 1°C, the
average temperature of the warmest month under +22 °C,
and about four winter months with average temperatures of
at least +10 °C [8]. The duration of sunshine varies between
1300 and 2000 hours, while the global radiation varies
between 780 and 1240 kWh/m². The location of the building
is shown in Fig. 2.
Fig. 2. Site plan of the University of Applied Sciences, Stuttgart.
The facade of the lecture room is oriented towards
Southwest. The test room dimensions are 10.1 x 9.7 x 3.8 m.
Fig. 3. shows floor plans of the building and the selected
spaces. The centre of each desk was taken as a measurement
point, this results in grid dimension of 1.60 m x 1.60 m in
the lecture hall. Window wall ratio of the lecture hall facade
is 40%. The room surface reflectances are: Rceiling = 80 %;
Rwalls =50 %; Rfloor = 20 %, Rfurniture = 50 %. The windows
are consisting of two layers of clear glass resulting in visible
transmittance (Vt) of 65 %.
Fig. 3. Floor plans of Building 3, 1st floor with computer laboratory, 2nd
floor with classroom.
III. LUMINANCE AND GLARE
EASUREMENTS of luminance and illuminance are
carried out together over a period of two months
(April, May 2015). The measurements are carried out every
week on one day in the morning, at noon and in the evening.
The HDR images are produced by reflex camera Rollei d 30
A
M
FIRST FLOOR
SECOND FLOOR
3
flex in combination with fisheye lens Nikon Fisheye
Converter FC-E8. The generated images are evaluated with
the LMK 2000 software.
The evaluations of the HDR images show the window is
a high glare because of its brightness. The contrast between
the screen and reflections from surrounding light causes the
glare. For this reason the glare should be eliminated.
A. Luminance ratios
The luminance measurements show, the requirements are
fulfilled in the classroom as well as in the computer lab.
According to Meyer, Francioli, & Kerhoven maximum
luminance ratios of 1:3 in the ergorama and 1:10 in the
panorama should be respected [9]. Fig. 4 shows as an
example the evaluated HDR image on April, 15th at nine
o'clock, where luminance ratios of 1:1.3:2.5 are given.
Fig. 4. HDR image with ergorama and panorama on April 15th at 9am.
B. Glare due to daylight
Daylight glare can be evaluated by the Daylight Glare
Probability (DGP) method. The glare probability means the
probability that an occupant in a certain position on a certain
date may be blinded. According to Wienold &
Christoffersen DGP can be calculated by the vertical
illuminance Ev, the illuminance of the glare source Ls, the
solid angle ωs and the Guth’s position index P [7].
(1)
with Ev Vertical illuminance at the observer’s eye [lx]
Ls Luminance of the glare source [cd/m²]
ωs Solid angle subtended at the observer’s eye [sr]
P Guth’s position index for every glare source
depending on the position of the window and the
line of sight of the observer [-]
The measured and simulated DGP on April 28th at 2pm in
the classroom ca be seen in Fig. 5. Neither via measurement
nor via simulation disturbing glare was detected (DGP < 35
%). To be able to give a comprehensive assessment for
glare, more measurements should be taken over the whole
year.
0%
10%
20%
30%
40%
50%
Measurement Simulation Diva
DGP [%]
Data collection [-]
Measurement Simulation Diva
Fig. 5. DGP in the classroom on April 28th at 2pm.
C. Glare due to artificial light
Artificial light glare can be assessed by the Unified Glare
Rating (UGR) method. According to CIE Publication 117-
1995 the UGR value is calculated by the background
luminance LB, the luminance of the glare source LS, the
solid angle ωs and the Guth’s position index P [10].
(2)
with LB Background luminance, calculated by Ev/π
with Ev as vertical illuminance at the observer’s
eye [cd/m²]
LS Luminance of the glare source [cd/m²]
ωs Solid angle subtended at the observer’s eye [sr]
P Guth’s position index for every glare source
depending on the position of the lamp and the line
of sight of the observer [-]
UGR values generally range from 10 to 30 where a high
value indicates significant discomfort glare, and a low value
indicates little discomfort glare. Electric lighting systems
producing UGR values of 10 or less are assumed to produce
no discomfort. Fig. 6 shows, neither in the classroom nor in
the computer laboratory disturbing can be detected. UGR
value is resulted less than 13 means there is no perceptible
glaring for the occupants.
0
5
10
15
20
25
30
Measurement Simulation Relux
UGR [-]
Data collection[-]
Measurement Simulation Relux
Fig. 6. UGR in the computer lab.
IV. ILLUMINANCE & DAYLIGHT COEFFICIENT
LLUMINANCE measurements were taken with HOBO
illumination/temperature data loggers. The sensors
dimension of 58 x 33 x 23 mm (width x depth x height).
This device has a range of 0 - 323000 lux and an accuracy
level of ±2.5 % at 25 °C. Figures 7-9 indicate a comparison
of illuminance measurements in the classroom and in the
computer laboratory under different sky conditions on
similar days.
I
4
The columns show the illumination level of each
measurement point. Unobstructed horizontal exterior
illuminance levels (Eh) were also measured and noted in the
diagram legends. Under cloudy sky, the illuminance level
do not greatly differs from first measurement row to rear
rows of the room. Under clear and sunny sky, the sensors
were affected by direct sun light and shadow in both lecture
halls. This effect causes high illuminance level differences
between measurements points and the one of the main
reason of the glare problems. Moreover, daylight
distribution was also observed under changing sky
conditions. Under sunny sky, the fluctuation is more
remarkable compare to cloudy sky conditions.
The measurements of illuminance, which were taken by
lux-meters, were compared with dynamic simulations. The
simulations were performed by Relux, Ecotect/Daysim and
Diva. These programs base on the ray tracing solver
Radiance that enables accurate and physically valid lighting
and daylighting simulations.
Fig. 6 gives an explanation for the fluctuating
illuminance in the measurement and simulation data. The
classroom which is located on the 2nd floor receives more
solar direct radiation than the computer. Due to shading
through the other building, there is a very small part of
direct radiation in the computer lab and a much higher part
of diffuse solar radiation. Therefore, we can say that the data
for the computer lab is quite accurate and reliable results can
be achieved.
Direct radiation
Diffuse radiation
Classroom
Computer laboratory
Fig. 6. Direct and diffuse solar radiation.
A. Illuminance
The comparison of the measured and simulated
illuminance is shown as follows: The dotted line shows the
average of the individual measurements. The other lines
show the simulated values. Fig. 7 shows there is a strong
variation in the illuminance under clear sky conditions in the
classroom because of high direct radiation. Under clear sky
in the computer laboratory as well as under overcast sky in
the lecture room and the computer laboratory, the curves
have a better correlation. According to DIN V 18599-10
classrooms and computer laboratories should have a
minimum task illuminance of 500 lx [11]. This standard is
largely met. For the areas with less than 500 lx an optimized
lighting system can be used. When sufficient daylight is
present, control system can turn off or reduce lighting in
steps or though dimming a slow, continues manner.
0
1000
2000
3000
4000
5000
6000
1 2 3 4
Illuminance [lx]
Table [-]
Measurement
individual value
Measurement average
(Eh=92.570 lx)
Simulation Relux
(Eh=64.100 lx)
Simulation Daysim
(Eh=62.951 lx)
Simulation Diva
(Eh=63.259 lx)
Fig. 7. Measured and simulated illuminance in the classroom under clear
sky on 28th April, 2 pm, table row 1.
0
1000
2000
3000
4000
5000
6000
1 2 3 4
Illuminance [lx]
Table [-]
Measuremet
individual value
Measurement average
(Eh=176.357 lx)
Simulation Relux
(Eh=47.600 lx)
Simulation Daysim
(Eh=66.870 lx)
Simulation Diva
(Eh=66.860 lx)
Fig. 8. Measured and simulated illuminance in the computer lab under
clear sky on 21st April, 12am, table row 2.
0
1000
2000
3000
4000
5000
6000
1 2 3 4
Illuminance [lx]
Table [-]
Measurement
individual value
Measurement average
(Eh=6.571 lx)
Simulation Relux
(Eh=9.530 lx)
Simulation Daysim
(Eh=8.684 lx)
Simulation Diva
(Eh=8.635 lx)
Fig. 9. Measured and simulated illuminance in the classroom under
overcast sky on 28th April, 8am, table row 2.
0
1000
2000
3000
4000
5000
6000
1 2 3 4
Illuminance [lx]
Table [-]
Measurement
individual value
Measurement average
(Eh=18.126 lx)
Simulation Relux
(Eh=17.200 lx)
Simulation Daysim
(Eh=15.505 lx)
Simulation Diva
(Eh=15.525 lx)
Fig. 10. Measured and simulated illuminance in the computer lab under
overcast sky on 15th May, 11am, table row 2.
B. Daylight coefficient
The daylight coefficient is calculated as ratio of illuminance
to exterior horizontal global illuminance.
(3)
with D Daylight coefficient [-]
Ei Illuminance [lx]
Eh Exterior horizontal global illuminance
5
Under overcast sky conditions (high diffuse radiation),
applicable results are displayed. The figures as well as the
illuminance diagrams show the requirement for a daylight
coefficient of 2 % is largely met till a room depth of 1.60 m
(table no. 2). An improvement could be achieved by
dimmable lamps.
0%
2%
4%
6%
8%
10%
12%
14%
16%
1 2 3 4
Daylight coefficient [%]
Table [-]
Measurement
individual value
Measurement average
(Eh=6.571 lx)
Simulation Relux
(Eh=9.530 lx)
Simulation Daysim
(Eh=8.684 lx)
Simulation Diva
(Eh=8.635 lx)
Fig. 11. Measured and simulated daylight coefficient in the classroom
under overcast sky on 28th April, 8am, table row 2.
0%
2%
4%
6%
8%
10%
12%
14%
16%
1 2 3 4
Daylight coefficient [%]
Table [-]
Measurement
individual value
Measurement average
(Eh=18.126 lx)
Simulation Relux
(Eh=17.200 lx)
Simulation Daysim
(Eh=15.505 lx)
Simulation Diva
(Eh=15.525 lx)
Fig. 12. Measured and simulated daylight coefficient in the computer lab
under overcast sky on 15th May, 11am, table row 2.
The indoor illuminance level due to artificial lighting only
(the measurement was conducted at night to avoid the
daylight) was measured. For the measurement HOBO data
loggers were positioned in the centre of each table. The
comparison of the measured and simulated illuminance
shows there is a 50 lux difference. Fig. 13 illustrate, there is
a high degree of agreement between measured and
simulated results.
0
50
100
150
200
250
300
1 2 3 4
Illuminance [lx]
Table [-]
Measurement
Simulation Relux
Fig. 13. Measured and simulated illuminance in the classroom, artificial
light, table row 1.
V. USER PROFILE
ITHIN the scope of this work the real occupancy
profiles for both rooms were analysed. Fig. 14
indicates that the classroom has an average use of 44 hours
per month (from October to June). During the semester
breaks the room is rarely used (from July to September). In
the winter months (from October to February) artificial light
about 44 hours is needed.
0
20
40
60
80
100
120
Jan. Feb. March April May June July Aug. Sept. Okt. Nov. Dec.
Time [h]
Daylight Artificial Light
Fig. 14. Occupancy profile of the classroom.
The occupancy profile of the computer lab follows similar
characteristics. As it can be seen in Fig. 15, the room was
not occupied from July to September. Compared with the
classroom it is obvious that the operation hours are less in
the computer laboratory. It should be mentioned the analysis
of the user profiles only refers to lecture periods. As the
computer lab is mainly used as public work-space for the
students, the real occupancy profile cannot be determined
exactly. As a result, it could be said that the real occupancy
profile is much higher than the here considered.
0
20
40
60
80
100
120
Jan. Feb. March April May June July Aug. Sept. Okt. Nov. Dec.
Time [h]
Daylight Artificial Light
Fig. 15. Occupancy profile of the computer lab.
Boundary conditions for different room types are
determined by DIN V 18599-10. Fig. 16 shows the
comparison of the real occupancy profile with the
occupancy profile of DIN V 18599-10. It can be noticed that
there is a difference of - 50 % in the classroom on day and a
difference of - 25 % on night.
749
69
1408
92
0
500
1000
1500
2000
2500
3000
Operation hours day Operation hours night
Operating time [h/a]
Real occupancy profile Occupancy profile DIN V 18599
Fig. 16. Real occupancy profile in comparison with DIN V 18599,
classroom.
Fig. 17 outlines the real occupancy profile in comparison
with DIN V 18599 for the computer lab. By comparing
these two profiles it is evident that the standard occupancy
results are exaggerated. On the other hand, for the real
occupancy profile, just the lecture times were considered.
Therefore, it is expected that the actual occupancy profile
can be estimated with 2000 operation hours per year.
W
6
165 1
2543
207
0
500
1000
1500
2000
2500
3000
Operation hours day Operation hours night
Operating time [h/a]
Real occupancy profile Occupancy profile DIN V 18599
Fig. 17. Real occupancy profile in comparison with DIN V 18599,
computer lab.
VI. ELECTRICITY CONSUMPTION
N order to capture the electricity consumption a long-term
measurement has been started. Fig. 18 shows the
measured data on July 1, 2015, which was s sunny day,
showing an outside temperature of 32 °C. Thus, it would be
expected that the room receives enough daylight in the
lecture hall and no artificial light is needed. However,
between 9am and 4pm there is an electric power use of
800W was detected. Having shading devises are not solved
the glare problems instead create either dark interiors and
force the using of artificial lighting. The room is equipped
with an opaque interior shading element, when its lowered
artificial lighting is as a supplement. The experiences
gaining from analysis, there is a considerable potential for
lighting- shading design optimization.
0
200
400
600
800
1000
1200
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00
Electric Power [W]
Time [h]
Fig. 18. Electric power consumption on July 1, 2015.
VII. SURVEY
T is a big advantage to have measured data and
comparable information about the investigated space but
nevertheless due to the complexity of the light environment
users` experiences and opinion is needed to better
understand the nature of the light and especially to discover
unpleasant occurrences such as glare, uniformity, use of
control systems etc. Because of that, the user satisfaction
questionnaires (IEA-SHC-Task 50 Subtask D3) were asked
students to fill in. All collected date were digitalized and
analysed in an excel sheet and the average score was find for
the questions.
A. Daylight
The results of the user survey are shown in Fig. 19. It
becomes clear that during daylight there is a high
optimization potential for the light sensitivity and for the
user-friendliness. In the computer lab the light distribution
and glare due to daylight are rated “good” whereas in the
classroom both criteria are rated “bad”. The illumination
condition is satisfying in both room types.
Light distribution Light sensitivity Illumination
condition
Glare User-friendliness
Classroom Computer laboratory
o
-
+
Fig. 19. User survey, daylight.
B. Artificial light
The results of the user survey on artificial lighting can be
found in Fig. 20. According to the users, there are glare
problems observed in both rooms. The illumination
condition is satisfying in the classroom as well as in the
computer lab. The light distribution is rated “good” in the
classroom; in the computer lab it could be better.
Light distribution Light sensitivity Illumination
condition
Glare User-friendliness
Classroom Computer laboratory
o
-
+
Fig. 20. User survey, artificial light.
VIII. ALTERNATIVES
A. Daylight Utilisation
ASED on the analysis, several improvement options
have been developed. The best of the 18 variants is an
external shading system with horizontal blinds covering the
whole width of the window, shown in Fig. 21.
Fig. 21. Horizontal blinds.
Fig. 22 and 23 show Daysim simulation results. At this point
it is important that the shading system in Daysim functions
as a static shading device. This means, the shading device
always is at the same position (lowered). The simulation set
with shading system supplies better glare protection but
result in mostly higher lighting energy consumption. The
daylight coefficient over 2 % can be improved by 18 % to
21 %. The daylight autonomy defines the percentage time of
I
I
B
7
a year that daylight by itself can supply the 500 lux
illuminance threshold of the occupied spaces. The daylight
autonomy can be improved by 50 %.
Fig. 22. Daylight factor and daylight autonomy of horizontal blinds in the
classroom.
The range of the useful daylight illuminance is given by
values between 100 and 2000 lx. It can be improved by 28
%. Glaring, indicated by values over 2000 lx, increase but
can be accepted by minimizing the energy consumption and
improving the daylight conditions.
Fig. 23. Useful daylight illuminance of horizontal blinds in the classroom.
B. Utilisation of artificial light
For the lighting control system different variants have been
assessed, including manual systems, presence controlled and
dimmable systems, which are shown in Fig. 24. The best
option is the combination of dimmable LED lamps
occupancy-off sensors. Compared to the current system the
lighting energy consumption can be decreased by 34 %.
15,6 14,3
26,5
15,0
12,7
23,8
0
5
10
15
20
25
30
Manual on/off
switch
Off occupancy
sensor
On/off occupancy
sensor
Dimming
(photosensor)
Dimming + off
occupancy sensor
Dimming + on/off
occupancy sensor
Specific energy demand [kWh/m²a]
Optimization Current system
19,3
19,0
20,8
18,9
26,4
Fig. 24. Energy consumption of different lighting control systems.
The implementation measures were also calculated by the
procedures according to DIN V 18599-4 [12]. The
comparable results are shown in Fig. 25. The evaluation
indicates that the measures are considered partly very
different by the three DIN methods. For the optimization
variation (occupancy-off sensor, dimmable LED lamps,
shading system via horizontal blinds), the best conformity is
given by the simulation with Daysim and the lumen method.
This leads to the conclusion that the implementation of the
proposed measures provides an energy saving of 30 - 40 %.
0%
20%
40%
60%
80%
100%
Occupancy
sensor
LED not
dimmable
LED dimmable 11 x T5 Straight
lamellas
Room
reflexion
Occupancy
sensor,
dimming, LED,
straight
lamellas
Lighting energy demand [%]
Tabulation method Lumen method Procedure for existing buildings Daysim (static shading) ReLight
Fig. 25. Implementation measures in the classroom.
Until now the implementation measures are very expensive
so that there is the need of finding a cheaper measure. This
could be for example the subdivision of the current shading
system so that only the area near to the blackboard can be
lowered. With this measure the room would get enough
daylight and the energy consumption could be reduced by
15 %. The proposed variant with horizontal blinds would
reduce the energy consumption by 34 %.
3% 0%
100%
0% 0%
100%
23%
63%
78%
5%
17%
85%
21%
50%
59%
28%
13%
66%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
DF>2% DA UDI100 UDI100-2000 UDI2000 Energy
consumption
Value [%]
Shading device (whole width) Shading device (only blackboard) Horizontal straight lamellas
Fig. 26. Saving potential.
IX. CONCLUSION
he relationship between energy efficiency and enhanced
daylight depends on the integration of controls into the
lighting system. Integrated lighting control systems based on
daylight would supply energy savings, as the electric energy
demand will decrease in response to sufficient amount of
daylight to provide the minimum illuminance level.
As a final conclusion it can be say that in both rooms the
required luminance ratios are met and there is no perceptible
daylight and/or artificial light glare problem. The
illumination condition is rated “good” by users. Moreover,
innovative daylighting systems can be used to enhance
daylighting indoors using available daylight. The lighting
energy consumption could be decreased from 6 to 4
kWh/m²a. Thus, the visual comfort for the occupants as well
as the lighting system expenses can be improved
significantly. The analysis has shown that with a presence
controlled lighting system with dimmable LED lamps an
energy saving of 30 - 40 % could be achieved. A maximum
user comfort could be generated by a shading system which
gives high daylight autonomy without glare. For this
purpose horizontal blinds were advised.
A cheaper alternative would be the replacement of the
current shading system for a shading system which can be
lowered independently (partial) near the blackboard. With
this measure 15 % of energy could be saved.
T
8
X. REFERENCES
[1] Ministry of Higher Education, Research and the Arts, “Press Release
Living Labs.” transformationszeitung 15th May, 2015, available
online at https://transformationszeitung.wordpress.com
/2014/05/15/reallabore-in-baden-wurttemberg/.
[2] A. Thewes, S. Maas, F. Scholzen, D. Waldmann, A. Zürbes, “Field
study on the energy consumption of school buildings in
Luxembourg”, Energy and Buildings, 68, pp. 460-470, 2014.
[3] J. Dobson, K. Carter, “An Attitudinal and Behavioural Study of
Scottish Pupils in Regards to Energy Consumption in Schools”, CIB
World Building Congress, United Kingdom, pp. 127-137, 2010.
[4] M. Appel, Save Energy Dollars with DOE Operations and
Maintenance Guide, School Business Affairs, 2010, pp. 27-30.
[5] W. K. E. Osterhaus, “Discomfort glare from daylight in computer
offices: What do we really know?,” Proc. of Lux Europa 2001,
Reykjavik, Iceland.
[6] M. Velds, “Assessment of Lighting Quality in Office Rooms with
Daylighting Systems,” PhD dissertation, Technical University of
Delft, Delft, The Netherlands, 2000, 209 pp.
[7] J. Wienold & J. Christoffersen, “Evaluation methods and
development of a new glare prediction model for daylight
environments with the use of CCD cameras,” Energy and Buildings
(in press, corrected proof, available online 24 April 2006).
[8] World Meteorological Organization, 2014, available online at
http://worldweather.wmo.int/en/city.html?cityId=1357.
[9] J.J. Meyer, D. Francioli, and H. Kerhoeven, “A New Model for the
Assessment of Visual Comfort at VDT-workstations.” Xth Annual
International Occupational Ergonomics and Safety, I, pp. 233-238,
1996.
[10] CIE Publication 117, “Discomfort Glare in Interior Lighting”
1995.
[11] DIN V 18599-10: Energetische Bewertung von Gebäuden –
Berechnung des Nutz-, End- und Primärenergiebedarfs für Heizung,
Kühlung, Lüftung, Trinkwarmwasser und Beleuchtung – Teil 10:
Nutzungsrandbedingungen, Klimadaten, Pre-Standard, Nov. 2011.
[12] DIN V 18599-4: Energetische Bewertung von Gebäuden –
Berechnung des Nutz-, End- und Primärenergiebedarfs für Heizung,
Kühlung, Lüftung, Trinkwarmwasser und Beleuchtung – Teil 4: Nutz-
und Endenergiebedarf für Beleuchtung, Pre-Standard, Nov. 2011.
XI. CURRICULUM VITAE
Amando Reber was born in Nürtingen, Germany,
on May 9, 1992. He received the Bachelor in Climate
Engineering from the University of Applied Sciences
Stuttgart in 2015. Since March, 2014 he is employed
at Drees & Sommer Advanced Building Technologies
GmbH and takes part in the funding programme.
Moreover, since March, 2015 he works in the Centre
of Applied Research on Sustainable Energy Technology, Stuttgart.
Dr. Dilay Kesten Erhart was born in Mersin,
Turkey, on July 28, 1980. She earned Diploma in
Architecture from Istanbul Technical University in
2003 but she had long been interested in energy and
building technology. While earning her master’s
degree in Environmental Control and Building
Technology, she went to Germany as an exchange
student and experienced a multidisciplinary field
known as building physics. Kesten Erhart joined EU Marie Curie Project
CityNet's team of PhD students working to develop an energy management
tool for urban areas and she got her PhD degree in 2012. As of July 2011
she is a Researcher at the Stuttgart University of Applied Sciences. She is
the author of more than 20 conference papers (published in proceedings)
and 3 scientific journal articles. She is also a reviewer of journal papers
submitted for publication and research projects. Currently she continues
working on the energy efficient lighting retrofit projects.
