LITERATURE REVIEW1 - INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/4977/8/08_chapter 2.pdffuzzy logic-based control algorithm to minimize thermal and artificial lighting energy

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


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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


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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


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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


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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


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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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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


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

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Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)Exterior diffuse horizontal Illuminance(Klux)

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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

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(a) (b) Fig. 2.8 Mean hourly (a) global illuminance; (b) diffuse illuminance for horizontal and vertical surfaces during December.

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(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.


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.


34

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


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.


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.

Documents

LITERATURE REVIEW1 - INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/4977/8/08_chapter 2.pdffuzzy logic-based control algorithm to minimize thermal and artificial lighting energy