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Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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CHAPTER 2
LITERATURE REVIEW1
2.1 Introduction
Daylighting controls are perceived as a lucrative option for daylighting to gain acceptance
and reverse the current practice of total reliance on artificial lighting. Substantial electrical
energy costs can be cut down if lighting controls are used in conjunction with automated
window blinds. Advances in SCT have stimulated significant research and development of
intelligent control algorithms pertaining to automated daylighting control systems. The
preliminary sections of this chapter examine the publications that discuss why manual
control of artificial lights and window blinds fail to achieve right management of daylighting
and does the artificial intelligence based automated daylighting control techniques meet
minimum standards for energy and human comfort performance. Apart from the literature
review, the final section of this chapter provides a bird's-eye view on the assessment of the
exterior daylight availability in a tropical climatic region with particular reference to
Bangalore (India) (latitude 12.97o N, longitude 77.56o E). In a nut shell, the objective of this
chapter is to provide an impression of the kinds and scope of the relevant existing research
efforts which forms the background for the present research study.
2.2 Manual Control of Artificial Lights and Window Blinds
In the present scenario, increased emphasis on energy efficiency and visual comfort has
brought particular attention to daylighting in commercial buildings. Even today, in most of A part of this chapter has been published in: (1)International Journal of Building Simulation, 2008, 1(2): 279–289. (2) International Journal of Building Simulation, 2009, 2(2): 85-94.
1. 1
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the commercial buildings sidelit windows are the only means of admitting daylight into an
interior. In order to capture the benefits of daylighting, a good fenestration design should
take into account an adequate task illumination, a window view to the outside environment
and visual comfort to the occupants. However, it is not easy to meet all these requirements.
A few daylighting design guidelines have made an attempt to make this design process
easier. According to the IESNA Lighting Handbook [11], the evolution of a specific design
involves the following steps: (a) revise the balance between luminance and illuminance
levels for better visual comfort and light quality, (b) design daylighting openings and
shading devices according to the necessity of direct or diffuse daylight in the space, (c)
control glare problems, and (d) review daylight-artificial light integration and control. In a
daylight-artificial light integrated scheme, control over the electric lighting and window
blind can be exerted by either manual control or automatic control or both. It is common
knowledge that the presence and actions of building occupants have a significant impact on
the energy performance of buildings. Various studies have been conducted in the past
decades to understand how building occupants interact with building visual control systems
such as the window blinds and the electric lights. Table 2.1 and Table 2.2 respectively
summarize the key findings [12-13] of previous field studies on the trends and patterns of
exercising manual operation of artificial lights and window blinds. Points in the Table 2.1
and Table 2.2 indicate that although the manner in which the users use their electric lights
and window blinds is consistent, yet not optimal to favour visual quality criteria and energy
conservation.
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Table 2.1 Key findings on the manual control of electric lights in the cited studies.
No. Findings Reference
1 In a small lighting control zone occupants tend to operate lights more
frequently in relation to the daylight availability.
Boyce (1980)[14]
2 • All lights in a room are switched on or off simultaneously.
• Switching mainly takes place when occupants enter or vacate a space.
Hunt 1979 [15]
Pigg et al. 1996 [16],
Love 1998[ 17]
3 Occupants exhibit either of the following two behaviours:
• Switching on the lights for the duration of the working hours and keep
it on even in times of temporary absence.
• Switching on the electric lights only when the interior is inadequately
lit by daylight.
Love 1998 [17]
4 There is a strong relationship between the propensity of switching the
lights off and the length of absence from the room. Occupants are more
likely to switch off the lights when leaving the space for longer periods.
Boyce 1980 [14]
Pigg et al 1996 [16]
5 The artificial light “switch on” probability on arrival exhibits a strong
correlation with minimum daylight illuminance in the working area.
Probability of “switching on” events is more common at lower threshold
illuminance, say 100lx. Above this level there is a considerable decrease
in switch on probability.
Hunt 1979 [15]
Reinhart and Voss 2003[18]
Lindelöf and Morel (2006)[19]
6 Occupants do not use their light dimmers to save energy but rather to
accommodate adequate illuminance for the tasks being performed.
Maniccia et al.[20]
7 Occupants do not switch off the electric lighting even when the indoor
illuminance is rather high because they fail to notice that it is switched
on.
Reinhart and Voss 2003 [18]
To accentuate energy performance and enhance human comfort, automated control
of electric lights and window blinds are an efficient and promising system to overcome
human inertia. Research has shown that savings of 30% to 50% are attainable for office
buildings that effectively utilize daylight-linked lighting control systems and automated
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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motorized window blinds [21]. Some degree of manual control is usually desirable but only
an automatic system can bring about certainty of energy saving [13].
Table 2.2 Key findings on the manual adjustment of window blinds in the cited studies.
No Findings Reference
1 • Blind operation rate vary greatly in relation to building orientation
• Occupants consciously set their blinds in a certain position
• Occupants manipulate shades mainly to avoid direct sunlight and overheating.
Rubin et al. 1978
[22], Rea 1984
[23], Inoue et al.
1988 [24].
2 • Occupants operate blinds more frequently on southern facades.
• Occupants are likely to accept that their blinds are extraneously opened than
closed.
Rubin et al 1978
[22], Lindsay and
Littlefair 1992
[25].
3 • Above a certain threshold of vertical solar irradiance on a façade (50 Wm-2), the
occlusion level of shades is proportional to the depth of solar penetration in a
room.
Inoue et al. 1988
[24].
4 • Occupants tend to change the position of the blinds when direct sunlight reaches
their work area. But, seldom reset the blinds for useful daylight admittance after
the unwanted glare conditions diminish.
Rea 1984 [23],
Lindsay and
Littlefair 1992
[25], Reinhart
2001[26],
2.3 Automated Shading Devices and Daylighting Controls
Owing to the flexibility and intuitive use, a great deal of attention has been paid in recent
works to harness the application of SCT like FL, ANN and GA to resolve Heating
Ventilation and Air Conditioning (HVAC) and lighting control problems in building
automation systems. Recently in building visual environment, many researchers as well as
international energy agency projects concede the benefits of artificial intelligence based
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automated light and wind blind control algorithms. The following sub-sections encompass a
review of such studies on the two key control systems affecting visual environment in
buildings playing a rule on energy conservation:
(a) Automated shading devices.
(b) Daylight linked lighting controls.
2.3.1 Automated shading devices
Daylight varies instantly depending upon the type of sky, sun position, climate, and window
orientation of a given site. The direct sunlight usually causes discomfort glare and more
overheating than diffused sky light. For this reason, shading provisions are often considered
for south, east and west facing window orientation [27]. As mentioned in the preceding
section, automated blinds offset the limitations of manually-operated blinds. Theoretically,
the benefit from the use of an automated blind system arises from the fact that blinds close
automatically when the interior becomes too glary or too hot, and re-open later to admit
useful daylight. Therefore, by adjusting their position in response to the exterior daylight
levels, automated motorized blinds are presumably able to protect the interior from glare and
overheating when there is a need for it and admit usable daylight soon after these conditions
subside.
Artificial intelligence based window blind controllers in building domain are
gradually becoming attractive over classical controllers. According to Bauer et al. [28] a
fuzzy logic-based control algorithm to minimize thermal and artificial lighting energy
demand in a building was first developed by the Technical University of Vienna. Because of
the algorithm limitation which focused only on energy conservation, authors [28] proposed
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the modification of the TU-Wien controller for achieving energy efficiency, as well as
thermal and visual comfort. The experiments were carried out in two south facing façade
office rooms with a floor area of 15.6 m2 and a window area of 3.77 m2. Using the fuzzy
logic-based smart blind controller, the experiments resulted in 11% lighting energy
reduction. In addition, the smart controller achieved artificial lighting energy saving of 8%
compared to a user who kept the blind half open all the time. Furthermore, the savings
attained increased up to 50% compared to the reference base case of a user who always kept
blinds closed.
The research work of Guillemin [29] put forward the user adaptive controllers for
integrated operation of blinds, electric lighting and HVAC. The authors [29] applied fuzzy
logic techniques for automated shading device controller capable of adapting to the user
behaviour and to the room characteristics. Self adaptation was achieved by means of GAs
that optimized the parameters of the fuzzy logic controllers. The function of shading device
was split into two parts depending on the user presence. With the detection of room
occupancy, priority was given to visual comfort. During unoccupancy priority was given to
thermal aspects (heating/cooling energy saving). The authors [29] developed the
aforementioned integrated system with three nested control loop levels. First loop (Level 1)
made the translation from the physical values to the appropriate commands of the
corresponding device. Second loop (Level 2) incorporated domain knowledge and used
adaptive models for realizing a smart global control strategy. The third loop allowed a
tuning of the Level 2 rule base using GA for continuous adaptation to the real building and
weather data conditions and to the user requirement and wishes. On detection of occupancy,
the controller switched to the visual optimization mode. In this mode, the algorithm was
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divided into two parts. The first part determined a maximum blind aperture in order to avoid
glare (using a fuzzy rule base) and the second part tried to find the blind position (below the
maximum value) that led to the inside illuminance corresponding to the illuminance set
point chosen by the user. When the room is unoccupied for a certain amount of time
(typically for 15 minutes at least) the controller switched over from the visual optimization
to the energy optimization algorithm.
In a daylight-artificial light integrated scheme, for optimizing visual comfort,
thermal comfort and energy consumption, Kurian et.al [30] proposed a fuzzy logic based
automated window blind controller suitable for tropical climate. The authors [30] considered
daylight on the window as the main fuzzy input for its correlation with the regional climatic
conditions rather than a universal climate blind model. A multistage fuzzy logic interface
has been presented for reducing the number of fuzzy rules. For occupants comfort and
energy conservation, human interactions are taken care of in the model in terms of
occupancy and user wishes. The proposed simulation models were developed and validated
in simulation environment by application of MATLAB/ SIMULINK fuzzy logic toolbox.
The fuzzy based blind controller was operated by three criteria: (a) visual comfort mode
(user present), (b) visual/thermal comfort mode (user present), and (c) energy optimization
mode (user absent). The integrated fuzzy blind controller with daylighting controls could
achieve 20% to 80% of annual energy savings compared to the base case of manual blind
systems without lighting control.
A similar attempt has been made by Kristl et.al.[31] for real time harmonization of
operations of thermal and illumination system in a test chamber located in Ljubljana,
Slovenia. In the experiment, a fuzzy logic regulator was programmed to adjust the position
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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of roller blinds and heating /cooling system in variation with outdoor solar radiation and
outdoor air temperature. Their experimental results [31] reveal the efficient functioning of
fuzzy controller in order to maintain visual and thermal comfort to set point value.
However, the authors have made no report of the quantitative energy savings achievable
with the system.
2.3.2 Daylighting controls
The role of daylight responsive dimming system is to preserve the resulting work plane
illuminance (daylight +dimmed electric light) at least equal to the desired illuminance
level[32]. In its basic form, as depicted in Fig. 2.1, it consists of three essential components:
photosensor, artificial lighting controller, and electronic dimming ballast [33]. Basically
commercial lighting controls prevalently employ either an open-loop or a closed-loop or
integral reset control algorithms for artificial light control operation [34-35].
Fig. 2.1 Basic scheme of photosensor controlled electric lighting system.
Window
Electric power
Photosensor
Dimming control signal
Lighting Controller
Dimming unit
Light fixtures + electronic dimming ballast
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In 1977-78 Crisp et.al.[36-37] reported a preliminary study on automatic artificial
light control in accordance with variation of daylight. The purpose was to supplement the
available daylight at the task area with just enough electric light to meet the design level.
The idea of computerized control of artificial light intended for daylight harvesting was
primarily introduced in 1987 by Crab et al.[ 38-39]. They developed a self commissioning
adaptive algorithm good enough for the real time prediction of natural light levels using the
external vertical plane illuminance measurements. This attempt of the authors [38-39] could
be viewed as a framework for the model based lighting control scheme.
Rubinstein et al. [40] presented a first documented demonstration of the closed loop
photocell control system that could correctly compensate for both, changes in daylight as
well as lumen depreciation of the electric lighting system. A novel two part photocell and
electronic dimming ballast capable of providing dimming range form 100% to 20% were
employed in the study. Their experimental results [40] showed a lighting energy saving of
approximately 50% due to integrated operation of daylighting, lumen maintenance and
scheduling.
Benefits of hybridization between simulation and machine learning can be
advantageously used for the purpose of light control. The reason is that such a controller
would progressively learn to adapt to building and environmental characteristics [29].
Gullemin and Morel [41-42] implemented an architecture of a lighting controller using GA
which could integrate itself into an advanced building control system according to user
wishes. In their process they compared three controllers, a manual control system, an
automatic controller without user adaptation, and an automatic controller with user
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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adaptation. The main benefit of automatic controllers was the reduction of the total energy
consumption with 26% energy savings compared with the reference case of manual control.
In a simulation work, Seongju Chang [43] established that analytical approaches
assisted by inductive learning aids the daylight responsive lighting control strategy. The
author [43] used multiple hybrid controllers to accomplish four control goals mainly
enriching the informational repertoire of systems control operations for lighting (by
inclusion of performance indicators for glare and solar gain), reducing the number of
sensing units necessary for capturing the states of the building’s visual performance
indicators in real time, enhancing the accuracy of predictions necessary for the identification
of the best control option, and maximizing the searches in the lighting system control state
space within a limited time. The resulting hybrid prototype control system Hybrid
Intelligence for System State Transition Operation (HISSTO) has been evaluated by the
author [43] to conform to specified visual performance indicators such as average
illuminance and uniformity. However, the energy conservation aspect is not investigated in
the research [43]. This seamless prototype controller using neural network was tested and
implemented through a web-based interface with a view to minimize data dependency and
sensor dependency [43]. Yet, it is found to be a complex strategy involving simulation
assisted ANN based control of integrated schemes.
The application of adaptive predictive techniques for dimming of artificial lights
employing Adaptive Neuro Fuzzy Inference Scheme (ANFIS) proposed by Kurian et al. [44]
shows a possibility of using a model based artificial light control technology [44]. An
attempt has been made by the same author in [10] for applying simulation assisted
computational model for adaptive predictive control in a daylight- artificial integrated
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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scheme for energy saving, visual comfort, and thermal comfort. For maximizing energy
saving while optimizing the performance and the quality of the visual environment the
author proposed an integrated scheme comprising of: (a) a system identification approach for
lighting control strategy, (b) a fuzzy logic based window blind controller to reduce glare,
increase uniformity and thermal comfort, and (c) an adaptive predictive control scheme for
the dimming of artificial light. In addition, the scheme was so designed as to coordinate and
control the automated electric lights as well as the window blind systems as per user
presence and user wishes. The authors simulation results [10] carried out for tropical
climates of Manipal, South India using Test Reference Year (TRY) 2005 showed that
ANFIS dimming with a fuzzy logic based window blind controller provided complete
optimization of thermal comfort and visual comfort including both glare control and
uniformity in the interior with an annual energy savings of 23% to 49%. While, ANFIS light
dimming with the fuzzy blind controller designed only for glare control showed an increased
annual energy saving of 35% to 60% according to window orientation. However, authors []
approach involved only simulation environment and training the controller with offline data
simulated from Radiance lighting software. Therefore, the possibilities of the controller
being effective under the varying performance requirements due to the parameter variations
and disturbances are limited. The authors [10] recommended that real time adaptive
predictive light control scheme modeled with real time measurements, online performance
predictors and design procedures would yield robust controller performance.
The potential of daylighting in the tropical regions has been recognized since the
1960s [45]. Before the daylighting is to be utilized as a building-environment technology it
is very important to consider the need for a daylight and sunlight availability database for
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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analyzing interior daylighting and energy performance characteristics of the system and
building [46]. Nevertheless, reliable prediction of daylight availability in indoor
environments via computational simulation, it requires a reasonably detailed exterior
illuminance model. Exterior daylight availability in terms of global and diffuse illuminance
value is absolutely essential while evaluating its energy saving potential in a daylight–
artificial integrated scheme and also in many interior daylight modelling and simulation
tools. Most fundamental daylight and solar research studies conducted by the architects and
engineers are based on the data taken from the meteorology stations. Exterior horizontal and
vertical daylight illuminance in particular, are recorded only at a relatively few weather
stations. Fortunately, metrological offices world-wide measure and archive exterior
horizontal global and diffuse irradiation data. These exterior irradiance datas may be most
advantageously employed for the prediction of horizontal and vertical daylight illuminance
using suitable daylight illuminance mathematical models[47-52].[47,48,49,50,51,52].
The concept is, using the established daylight illuminance models if luminous
efficacy which is the ratio of illuminance to irradiance is computed, the measured irradiance
values could be converted into illuminance values which in turn could be used as an input
for the daylight simulation tool to calculate the daylight availability [53-54]. The following
sections depict the preliminary part of the research work carried out to arrive at a
comprehensive idea on the assessment of exterior daylight availability and its characteristics
considering a representative case of Bangalore. With the monthly mean hourly climate data,
the assessment of the daylight illuminance on a horizontal window and vertical windows
facing four cardinal directions(north, east, south, and west) is worked out using Perez et al.
inclined irradiance model [48] hereafter Perez model.
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2.4 Estimation of Exterior Daylight Availability
Taking the representative illustration case of Bangalore, the idea is to present the analysis
and results of the work on prediction and estimation of: (a) hourly exterior horizontal global
and diffuse luminous efficacy, (b) hourly horizontal and vertical global as well as the diffuse
illuminance on four cardinal directions (north, south, east, and west), and (c) computation of
sky ratio. To estimate the exterior illuminance according to Perez model, indispensable input
parameters are: average hourly global and diffuse horizontal irradiance (W/m2); mean hourly
outdoor temperature (oC) and relative humidity (%). To aid the present study, these
climatological input data sets associated with Bangalore region for the year 2001, were
furnished by the India Metrological Department (IMD), Pune. (Refer Table A1.1 and Table
A1.2 of Appendix-A1).
Perez model provides a general calculation technique for the exterior diffuse
illuminance incident on building facades, various surface orientations and inclinations. The
basis for preferring Perez model for the current study is: (a) the model looks particularly
interesting as it encompasses the weather conditions of Bangalore and includes the
parameters that describe well different sky types of the region [55], and (b) Literature
reviews presented by Muneer [56-57], Vartianen [49]; Chirarattananon [51] point out how
the Perez model fits well on various climatic solar radiation measurements. Perez model
comprises of (a) luminous efficacy model to forecast the horizontal global and diffuse
illuminance from the corresponding irradiance data, and (b) slope illuminance model to
evaluate the illuminance on the vertical and sloping surfaces respectively from the
horizontal illuminance estimate.
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Equation (2.1) represents the Perez model for the calculation of horizontal global and diffuse
luminous efficacies using the appropriate four variable coefficients:
∆+++= lncos iiiihf dcWbaK ξ (2.1)
Subsequently, exterior horizontal illuminance is given by:
hfhfhf KIE = (2.2)
where, the subscript f in Eqs. (2.1) and (2.2) is designated to indicate either g or d
respectively.
According to the Perez model, the hourly diffuse illuminance βdE (lx) on an inclined surface
with a tilt angle of β is:
( )( )
+
+−+= βββ sin1cos15.0 211 F
b
aFFEE dhd (2.3)
To calculate the global illuminance on a tilted surface βgE , Eq. (2.5) proposed by [58] for the
global irradiance on a sloping surface βgI is initially determined by using Eq. 2.4.
( ) ( )( )βρβξ
β cos15.0cos15.0cos
cos−++++
Θ=
hdbgdhbg IIIII (2.4)
Consequently, βgE is estimated by :
ββ gghg IKE = (2.5)
Table 2.3 Classification of sky condition [58].
Values of indices
Sky conditions Sky ratio )(SR Clearness index (ε ) (Bin No.)
Clear SR ≤0.3 ε ≥4.5 7−8
Partly cloudy (or intermediate) 0.3< SR < 0.8 1.23<ε <4.5 3−6
Cloudy (or overcast) SR ≥0.8 ε ≤1.23 1−2
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SR and ε have been used as the indicators of sky condition. IESNA [11] and Perez classify
[59] sky into three conditions according to the values of the sky ratio and the clearness
indices as summarized in Table 2.3. The values of indices as highlighted in Table 2.3 are
organized into ten ranges (from 0 to 1.0) specific to sky ratio and eight bins for the clearness
index [11, 59]. The relationship between SR and ε is indicated in Eq. (2.7). This
relationship along with a constant term C enables the calculation of either SR or ε if one of
the parameters is known [60].
gh
dh
E
ESR = (2.6)
( )CSR +=
1ε (2.7)
Equations (2.1) to (2.7) are utilized for the estimation of exterior daylight availability
in Bangalore region as detailed in the next sub-section 2.4.1.
2.4.1 An Overall view of exterior daylight availability in Bangalore
This section covers the results of the exterior daylight availability in a tropical climatic
region considering the representative illustration case of Bangalore. Further, daylight
illumination is invariably assessed using the functional form of Perez all weather model.
Figures 2.2(a) and 2.2(b) respectively portrays the user friendly MATLAB Graphical
User Interface (GUI) window display for the estimation of exterior daylight availability.
Table A1.1 and Table A1.2 of Appendix A1 demonstrate the monthly mean hourly values of
the global and diffuse radiation during the pre monsoon (March to May), monsoon (June to
September) and post monsoon (October to February) seasons. It is evident from Table A1.1
that the region receives maximum global radiation during the month of May (summer) and
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minimum during July (monsoon). Further, Table A1.2 indicates that minimum diffuse
radiation is during the month of December and maximum during the month of June.
Figures 2.3(a), 2.4(a) and 2.5(a) portray the contour plots of the global irradiance, global
luminous efficacy and global horizontal illuminance respectively. Likewise, Fig. 2.3(b),
2.4(b) and 2.5(b) depict the contour plots of the diffuse irradiance, diffuse luminous efficacy
and diffuse horizontal illuminance respectively. It is noted from these plots that the
variations in day length are meagre throughout the year and differ appreciably from those of
temperate zones [61]. The mean value of daytime varies from 11.5 hours to 13 hours with
the longest day of the year being on summer solstice (June 21st) and the shortest day on
winter solstice (December 21st). As characterized in Fig. 2.3(b), Bangalore receives
relatively uniform diffuse radiation throughout the year for the reason that it is more or less
uniformly cloudy throughout the year. Comparison of Fig. 2.4(a) and Fig. 2.4(b) evidently
illustrates that the annual diffuse luminous efficacy is higher than the global luminous
efficacy demonstrating the potentiality of sky type of the province for energy efficient
daylight harvesting in buildings. Additionally, diffuse radiation is the highest for the
duration 10:00- 16:00 hours during the summer and monsoon months (essentially May to
August) due to the increased turbidity and cloudiness during these months and least during
the clear winter months. At the same time, due to high solar altitude during the months of
July –August, global luminous efficacy is high during these periods. The estimated annual
mean global and diffuse luminous efficacy is 105 lm/W and 120 lm/W respectively.
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(b) (a)
Fig. 2.2 Generated simulation tool for the estimation of outdoor daylight availability. (a) GUI window displaying tabulated data of global as well as diffuse irradiance and their related contour plots (b) Vertical illuminance plot for the month of June.
(a) (b) Fig. 2.4 Contour plot of: (a) Global horizontal luminous efficacy (lm/W), (b) Diffuse horizontal luminous efficacy (lm/W).
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rs)
Tim
e(h
rs)
Tim
e(h
rs)
Tim
e(h
rs)
Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
7
8
9
10
11
12
13
14
15
16
17
18
0
20
40
60
80
100
120
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
31
Table 2.4 and Table 2.5 tabulate the computed mean hourly horizontal global and diffuse
illuminance respectively. Table 2.4 reveals that an increase in the global illuminance takes
place during the months of January and February, progressing towards maximum
illuminance during summer from March to May. However, due to the onset of south west
monsoon in June, a significant drop in the global radiation declines the global illuminance.
On the other hand, as a result of increased cloudiness and advance of the monsoon, a
progressive increase in diffuse illuminance takes place during June to August as shown in
Table 2.5 with a peak at June. Normally, the region receives a total mean annual global and
diffuse illuminance of approximately 49.3Klx and 22.2Klx respectively pointing to the
likely benefits that daylight harvesting could attain.
Table 2.4 Estimated mean hourly horizontal global illuminance (lx). Time
Month 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00
Jan. 0 2247 16416 38306 59076 74447 83603 82283 75366 58956 37534 17088 3510
Feb. 0 4578 19548 42000 65826 81514 93386 88712 77928 61838 41850 18924 5050
Mar. 0 5060 23112 47197 72160 89152 93856 93795 83276 67946 44472 21888 6080
Apr. 0 6380 23754 49377 72380 86025 91242 88880 81859 62167 50694 29100 8656
May 672 9918 30030 53872 78888 97208 105096 104748 95372 75864 50232 26320 1112
Jun. 770 6726 20056 37278 54609 64130 68420 76518 59724 46216 34884 20615 6474
Jul. 394 5593 17331 32373 42846 54279 58016 62937 57750 47615 29767 15642 5125
Aug. 428 2484 14933 34992 53790 58740 66272 57240 53318 42536 31007 15322 3038
Sep. 0 1908 16416 36273 54827 65073 74736 68908 56805 44393 26235 13468 3180
Oct. 0 1554 15151 34776 53179 66233 75756 77486 69871 54998 35934 17745 2850
Nov. 0 7616 18857 34226 47952 58800 65670 65400 64310 43442 30282 19008 5768
Dec. 0 2691 14933 35316 54279 59808 68704 59360 55330 44172 31928 15648 4560
Table 2.5 Estimated mean hourly horizontal diffuse illuminance (lx).
Time
Month 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 Jan. 0 984 5425 14241 20961 26600 28427 31122 27559 23424 15428 5952 1771 Feb. 0 1008 5355 13938 19890 25000 26691 30134 27125 24768 17822 8370 817 Mar. 0 1700 9408 17500 23564 27336 28485 28676 28427 23530 16758 7198 780
Apr. 0 2210 11088 19872 26264 32110 33669 33282 29640 24180 19000 9204 2387 May 508 2703 12126 20286 26852 28704 31144 30328 28676 26200 19177 12420 2503
Jun. 497 2158 13386 26000 37422 43092 46500 46494 39875 32736 22692 12051 3311 Jul. 417 1859 13426 25152 34056 39936 47492 46863 39625 30114 19908 9000 2058
Aug. 400 1690 12371 25545 33930 42672 43434 44100 37211 29972 19278 8250 1222 Sep. 0 1503 10998 24156 32500 38220 39474 39552 34816 26316 17024 7920 1152 Oct. 0 1494 6776 17250 22605 29868 34163 35250 33087 25000 14994 6608 1096 Nov. 0 1264 5967 15456 21372 25728 32766 34100 31944 23912 13908 5355 1120
Dec. 0 498 3825 10296 14522 18753 20698 20592 17685 14000 9344 3300 500
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
32
(a) (b) Fig. 2.6 Percentage of normal office working hours (8.00a.m- 5:00p.m) for which outdoor horizontal illuminance is exceeded computed for (a) horizontal global illuminance, (b) horizontal diffuse illuminance .
0000 10101010 20202020 30303030 40404040 505050500000
10101010
20202020
30303030
40404040
50505050
60606060
70707070
80808080
90909090
100100100100
Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)
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%)
0000 20202020 40404040 60606060 80808080 100100100100 1201201201200000
10101010
20202020
30303030
40404040
50505050
60606060
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80808080
90909090
100100100100
Exterior global horiz ontal Illuminance(Klux)Exterior global horiz ontal Illuminance(Klux)Exterior global horiz ontal Illuminance(Klux)Exterior global horiz ontal Illuminance(Klux)
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%)
off
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%)
(a) (b) Fig. 2.7 Mean hourly (a) global illuminance, (b) diffuse illuminance for horizontal and vertical surfaces during June.
6 8 10 12 14 16 180
1
2
3
4
5
6
7
8x 10
4
Time(hrs)
Illu
min
an
ce
(lu
x)
Global illuminance for June month
Horizontal
West
East
North
South
6 8 10 12 14 16 180
1
2
3
4
5x 10
4
Time(hrs)Illu
min
an
ce
(lu
x)
Diffuse illuminance for June month
Horizontal
West
East
North
South
6 8 10 12 14 16 180
0.5
1
1.5
2
2.5x 10
4
Time(hrs)
Illu
min
an
ce
(lu
x)
Diffuse illuminance for December month
Horizontal
West
East
North
South
6 8 10 12 14 16 180
1
2
3
4
5
6
7
8x 10
4
Time(hrs)
Illu
min
an
ce
(lu
x)
Global illuminance for December month
Horizontal
West
East
North
South
(a) (b) Fig. 2.8 Mean hourly (a) global illuminance; (b) diffuse illuminance for horizontal and vertical surfaces during December.
clearclearclearclear intermediateintermediateintermediateintermediate cloudycloudycloudycloudy0000
10101010
20202020
30303030
40404040
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Sky conditionSky conditionSky conditionSky condition
Pe
rce
nta
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Pe
rce
nta
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Pe
rce
nta
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Pe
rce
nta
ge
17
81.8
1.2
44.3
53.86
2.01
εεεε
sky ratiosky ratiosky ratiosky ratio
0000 0.10.10.10.1 0.20.20.20.2 0.30.30.30.3 0.40.40.40.4 0.50.50.50.5 0.60.60.60.6 0.70.70.70.70000
2222
4444
6666
8888
10101010
12121212
14141414
16161616
18181818
20202020
Sky ratio Sky ratio Sky ratio Sky ratio
cle
arn
ess
in
de
xc
lea
rne
ss
in
de
xc
lea
rne
ss
in
de
xc
lea
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ss
in
de
x
(a) (b) Fig. 2.9 (a) Sky conditions according to the value of Sky ratio (SR) and Perez’s clearness index (ε), (b) Relationship between SR and ε.
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
33
Fig. 2.6(a) and 2.6(b) show the frequency distribution of global and diffuse horizontal
illuminance respectively during normal office working hours (8.00a.m to 5.00p.m) and the
percentage of normal office working hours for which a certain outdoor horizontal
illuminance exceeds above a certain threshold. Such frequency plots facilitate forecasting of
annual energy savings by practical implementation of lighting controls in the daylight–
artificial light integrated scheme. It can be seen from plots of Fig. 2.6, for approximately
more than 75% of the time in a year, the exterior illuminance surpass 10 Klx implicating a
daylight factor exceeding 3% with the interior design illuminance of 300lx.
Building orientation is an important parameter for a climate-responsive structure.
The amount of daylight received by a building is determined by its orientation. Tables 2.6
and 2.7 illustrate computed global and diffuse illuminance as a function of façade
orientation in four cardinal directions [south, east, north and west] respectively. It is noted
from Table 2.6 and 2.7 that in comparison with all other months, in the month of winter
solstice, due to low solar elevation the southern facade receives maximum global
illuminance (45.6 Klx) and diffuse illuminance (15.3 Klx)) compared to the horizontal
surface (global=34.3 Klx and diffuse=10.3 Klx) for the same month. On the contrary, during
summer solstice when the sun is at its northern most high altitude, the northern façade is
exposed to more radiation and as a result there is higher illuminance (global =22.9 Klx and
diffuse=11.8 Klx) when evaluated with all the other months except the value is lower than
the horizontal surface illuminance (global =38.1Klx and diffuse=25.0Klx) for the same
month.
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
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Table 2.6 Estimated mean vertical global Table 2.7 Estimated mean vertical diffuse illuminance (lx). illuminance (lx).
Month North South East West
Jan. 10530 38214 24231 23989 Feb. 11038 32765 27819 27550 Mar. 15725 32091 29985 28895 Apr. 15012 20063 28279 28091 May 18381 16320 29500 29410 Jun. 22913 18189 26660 26395 Jul. 19381 19492 20889 19590 Aug. 17156 19200 20276 20275 Sep. 16959 21284 23323 23320 Oct. 12705 33584 27819 27550 Nov. 11184 34224 24304 24056 Dec. 9537 45652 23246 23105
Figures 2.7 and 2.8 exhibit the simulation results of the illuminance during the
months enclosing summer solstice and winter solstice respectively. Especially under the
cloudy skies, the dynamic illuminance may change within an hour when very unstable
conditions prevail. Due to this, the hourly average may represent an untrue stable level
during the hour. Neglecting the dynamic variations within an hour, the graphs of Figs. 2.7
and 2.8 are outlined with continuous lines. As detailed earlier, comparison of plots depicted
in Figs. 2.7 and 2.8 illustrate that the south facing facade receives higher amount of
illuminance in December rather than in June. Further, from the plots it is noticed that the
illuminance on east and west facade are almost symmetrical and northern facade has almost
similar characteristics of east and facing surfaces. However, south, east and west facing
windows pose glare problems during forenoon and evening period. Although the magnitude
of glare caused by the south facing window to some extent is lower than the east and west
facing window, the north facing window receives uniform diffuse light throughout the day.
Accordingly, there is a need to cut out glare in south, east as well as in west facing windows
by proper selection of shading devices [30]. However, from the daylighting and air
conditioning point of view, buildings with south facing windows are conducive to trim down
Month North South East West
Jan. 5678 13722 8912 8900 Feb. 6958 12995 9772 9750 Mar. 6642 11336 11035 11033 Apr. 7720 9136 10784 10750 May 7134 6642 11223 11121 Jun. 11814 10645 13677 13665 Jul. 10925 10903 13285 13250 Aug. 10583 12109 12381 12380 Sep. 8229 14137 12023 12010 Oct. 6767 14492 9848 9772 Nov. 5795 15402 8310 8229 Dec. 5126 15327 7511 7473
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
35
cooling load in summer and heating load in winter [19]. The reason may be attributed to the
solar incident angle which is much larger in summer compared to winter for a south facing
wall.
Fig. 2.9(a) depicts a bar chart to illustrate the sky condition of Bangalore region
classified on the basis of sky ratio and Perez’s clearness index. As depicted in Fig. 2.9(a) the
sky is mostly clear with occasional presence of low, dense clouds during summer. Figure
2.9(b) shows the relationship between sky ratio and Perez clearness index. As mentioned
previously, the relationship between the sky ratio and Perez clearness index may be
advantageously utilized for predicting the other indices when one index is known. Using the
expression (2.7), the estimated constant C =0.002 for Bangalore. Clearly, luminosity and the
energy from the sky on a horizontal plane in the area greatly favour daylight harvesting.
2.5 Chapter Summary
This chapter has reviewed the published literatures on the manual and automatic daylighting
controls coupled with the dynamic shading devices playing a rule on energy conservation.
Compared to the reference scenario of manual control, a dimmed lighting system promises
unrealistically high energy savings as the lighting sensor responds actively to the available
daylight while the occupant does not. Though the application of soft computing techniques
in building automation domain are still in its early phases, recent studies indicate their
effectiveness in control process leading to elevation in energy savings and optimization of
visual comfort.
Adaptive Predictive Lighting Controllers for Daylight Artificial Light Integrated Schemes
36
The performance of daylighting technologies depends on the dynamic nature of the
external illuminance. Daylight on windows delivers a starting datum for the quantitative
estimation of interior daylight illuminance in buildings. For computing either the mean
hourly exterior illuminance or daylight on window, according to daylighting literatures most
work to date has been based on Perez model. Moreover this technique which acknowledges
the influence of both the global and diffuse illuminance is conducive to do further prediction
of interior daylight illuminance. An assessment of the exterior daylight illuminance for
Bangalore region implicate that there is usable daylight throughout office hours on every
day of the year. In essence, the background discussed in this chapter provides a preliminary
starting point for the forthcoming endeavours discussed in the subsequent chapters. This
entails development of suitable interior daylight illuminance prediction models and devising
of daylighting control systems in commercial buildings.