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International Journal of Sustainable Energy
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Estimation of daylight availability in Kolkata
and approximation of indoor daylight levels for
different daylighting methods
Sourin Bhattacharya, Sudipta Majumder, Subarna Roy & Imran Hossain
Sardar
To cite this article: Sourin Bhattacharya, Sudipta Majumder, Subarna Roy & Imran Hossain
Sardar (2022) Estimation of daylight availability in Kolkata and approximation of indoor
daylight levels for different daylighting methods, International Journal of Sustainable Energy,
41:1, 29-57, DOI: 10.1080/14786451.2021.1894145
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Estimation of daylight availability in Kolkata and approximation
of indoor daylight levels for different daylighting methods
Sourin Bhattacharya a, Sudipta Majumder b, Subarna Royc* and Imran Hossain Sardard
aTransport Department, Government of West Bengal, Kolkata, India; bTata Consultancy Services Ltd., Kolkata, India;
cLighting Researcher and Consultant, Kolkata, India; dChhapna High Madrasah, Kolkata, India
ABSTRACT
This work estimated the daylight availability and luminous efficacy of solar
illuminance and approximated indoor daylight levels for various indoor
daylighting methods for Kolkata, a major city in eastern India having
tropical savanna climate, by applying two computational methods: the
Perez model and the IESNA recommended calculation procedure. It was
found that hourly horizontal diffuse and global illuminance, horizontal
sky and total daylight illuminance values remained mostly over 10 klx
throughout the year, and global and diffuse luminous efficacies mostly
remained around 95–120 and 110–150 lm/W, respectively. The linear
correlation between solar irradiation and estimated illuminance was
stronger for Perez model. Thus, the application of Perez model was
considered the better choice to appraise the daylighting potential of
Kolkata.
ARTICLE HISTORY
Received 28 October 2020
Accepted 16 February 2021
KEYWORDS
Daylight availability; solar
illuminance; daylighting
devices; global luminous
efficacy; diffuse luminous
efficacy
1. Introduction
Solar radiation is a great source of renewable energy worldwide and an estimated four million exa-
joules (4 × 106 EJ) of solar energy is received annually by the earth of which fifty-thousand exajoules
(5 × 104 EJ) could potentially be harnessed by current technology (Kabir et al. 2018). Proper utilis-
ation of solar energy helped the human civilisation to develop and prosper through various ages.
Today, harnessing its potential could have various facets in daily life and it would prove to be an
advantageous choice over conventional fossil fuel-based power systems (Kalogirou 2004). It
could considerably reduce global carbon emissions (Shahsavari and Akbari 2018), solar and bio-
mass energy-based hybrid combined cooling heating and power systems (CCHP) could be driven
at a greater system energy efficiency (Wang and Yang 2016), remote areas could avail basic utilities
of electrification (Chakrabarti and Chakrabarti 2002) and street lighting systems could employ
LEDs to efficiently utilise the stored electrical energy in battery banks, harnessed from solar radi-
ation (Dalla Costa et al. 2010), which would advance the recent developments pertaining to
energy-efficient road lighting planning that is currently focused on road classification (Chakraborty
et al. 2018).
Solar energy not only can be used for electricity generation and heating but also can be used for
daylighting purposes. Daylight is the ‘holistic combination’ of the luminous characteristics of sun-
light and skylight (Knoop et al. 2020) and is termed as a ‘key design driver’ (Turan et al. 2020) in the
commissioning of construction projects. It is of significant importance to architects and lighting
designers to reduce the net electrical load associated with lamps and necessary apparatus, such
as ballasts, control gears, dimmers, etc. and adaptive lighting and shading control techniques
© 2021 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Sourin Bhattacharya sourinrcb@ieee.org
*Present address: SARN Solar Solution Pvt. Ltd.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY
2022, VOL. 41, NO. 1, 29–57
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may assist efforts to reduce energy consumption in lighting systems (Bisegna et al. 2016). Extensive
research works have been conducted on the minimisation of energy consumption of lamp-based
electrical loads in cost-optimal nearly zero energy buildings (ZEBs) (Pikas, Thalfeldt, and Kurnitski
2014; D’Agostino and Parker 2018; Doulos et al. 2019) and daylighting is seen as a potent tool to
lower the electrical energy consumption of artificial illumination systems (Okogbue, Adedokun,
and Holmgren 2008) as overall requirements of illumination could be met by controlling artificial
illumination systems according to available indoor daylight levels (Alrubaih et al. 2013).
Daylight is considered to be the best source of illumination for colour rendering, it can provide
110 lumens of luminous flux per Watt of solar radiation (a luminous efficacy of 110 lm/W) and its
spectra closely match the human spectral visual response (Kandilli and Ulgen 2008). However, the
luminous efficacy of daylight varies with sky clearness, solar altitude and atmospheric conditions. It
is 70–105 lm/W for direct sunlight, approximately 130 lm/W for the clear sky and around 110 lm/
W for the overcast sky under diffuse skylight (Littlefair 1985). Furthermore, daylight is a natural,
temporally variant (Ferenčíková and Darula 2017), dynamic source of illumination (Deroisy and
Deneyer 2017) and its spectral power distribution (SPD) for the 330 –700 nm wavelength range
was measured by Henderson and Hodgkiss (1963). The CIE Standard Illuminant D65 is a hypoth-
esised illuminant to simulate the naturally occurring standard illumination conditions under day-
light and attempts have been made to design and simulate the D65 (Powell 1996; Lam and Xin
2002). Moreover, the human circadian clock is entrained to sunlight or more specifically the 24-
h solar cycle (Duffy and Wright Jr 2005; Roenneberg et al. 2013; Woelders et al. 2017) and exposure
to daylight assumes much importance in the everyday life of human beings since human health and
wellness are intricately linked to it and various studies have indicated that daylight is preferred to
artificial illumination in different settingsor fields of human activity (Markus 1967; Cuttle 1983;
Heerwagen and Heerwagen 1986; Galasiu and Veitch 2006).
Currently, there is an impetus on daylight integration and control in commercial buildings, resi-
dential spaces and other indoor environments (Colaco et al. 2008; Xue, Mak, and Cheung 2014;
Darula 2018; Kose and Kazanasmaz 2020) and the performance of different types of windows
have been evaluated, by experimentation or simulation. Fasi and Budaiwi (2015) used the ‘Design-
Builder’ computer program to simulate an office space under a hot climate and found that the build-
ing energy consumption reduced for all the types of glazing of the window models. It was further
reported that automatic interior shading control achieved visual comfort and did not increase
energy consumption. Kousalyadevi and Lavanya (2019) utilised the ‘Velux’ software to simulate
the daylighting conditions of an industrial building and measured the energy consumption. It
was found that the maximum daylight illuminance level was 902.3 lx and the energy-saving poten-
tial (ESP) could reach up to 31.4% for dimming control of artificial lighting with a top daylighting
panel. Ghisi and Tinker (2005) used the ‘VisualDOE’ computer program to simulate the climatic
conditions of Leeds, United Kingdom and Florianopolis, Brazil and proposed a methodology of
prediction of energy savings integrating the concepts of ‘Ideal Window Area’ and ‘Daylight Factors’.
Al-Ashwal and Hassan (2017) investigated the potential of energy savings by daylight integration
with electrical lighting for a classroom scenario in a tropical climate using the ‘Energy Plus’ com-
puter program. It was concluded that electrical energy consumption for artificial illumination could
be reduced by 28–40% which is 10–18% of the total energy consumption. Azad, Rakshit, and Patil
(2018) measured daylight in New Delhi, India and characterised global and diffuse luminous
efficacy patterns. Their work compared four efficacy models and ameliorated the outputs using
optimised coefficients, adopted from the measured data. Variations in global and diffuse luminous
efficacy values were presented for clear, intermediate and overcast sky conditions and average
monthly global and diffuse illuminance values were estimated with original and modified efficacy
models. A similar work estimated global and diffuse luminous efficacy for the tropical region of
Bangkok, Thailand, proposed two empirical all-weather efficacy models and validated the same
(Chaiwiwatworakul and Chirarattananon 2013). Dieste-Velasco et al. (2019) compared the per-
formance of 18 luminous efficacy models with a proposed model utilising measured global
30 S. BHATTACHARYA ET AL.
illuminance values for varied sky conditions in Burgos, Spain and concluded that the proposed
model fared well under varied sky conditions. A pioneering work conducted by Bellia and Fra-
gliasso (2017) proposed new parameters, such as Daylight Integration Adequacy, Percentage
Light Deficit (PLD), Percentage Intrinsic Light Excess (PILE) and Percentage Light Waste
(PLW), to analyse the performance of daylight-linked artificial illumination control systems and
presented a simple case study in support of their proposition.
Nevertheless, daylight integration in artificially lit indoor environments would require careful
architectural planning and availability of daylight data for all the regions in which such exercise
shall be carried out. However, there is a dearth of literature on solar illuminance data for various
weather stations in India and as such, estimation of solar illuminance levels should be done
from meteorological data, solar insolation maps (Rao and Seshadri 1961) and predicted mean
diffuse solar radiation tables (Karakoti, Das, and Singh 2012). Using the 2014 SUNY semi-empirical
model solar irradiance data of National Solar Radiation Database (NSRDB), National Renewable
Energy Laboratory (NREL 2014; Sengupta et al. 2018), this work estimated the solar illuminance
levels of Kolkata, India (22.5726° N, 88.3639° E) with Oracle software using two computational
methods: the Perez model (Perez et al. 1990) and the IESNA recommended calculation procedure
of estimating daylight availability (IES Calculation Procedures Committee 1984); and compared the
simulated results with results obtained from photometric measurements that were carried out in
April 2020. The calculated daylight data were utilised to assess indoor daylight illuminance levels
for four different daylighting devices: motorised window shading, light pipe, skylight and light
shelf. The objectives of this work are to assess the consistency of simulated photometric outputs
(solar illuminance levels) with measured photometric outputs, estimate indoor daylight illuminance
levels for various daylighting devices, and commentate on the reliability of the two computational
methods that have been used.
2. Methodology
2.1. Geographical description and collection of solar irradiation data
Daylighting of buildings requires careful consideration of daylight availability and prediction of
daylight availability is dependent upon a number of factors, such as geographical location, climate,
altitude, atmospheric turbidity, cloud cover and cloudscape. Kolkata, a major city in eastern India,
has a tropical savanna climate and receives maximum rainfall from June to August. The winter lasts
from November to February. Solar radiation data of Kolkata, obtained from the NREL database,
was utilised to facilitate daylight computations. Table 1 provides a compendium of ten years
(2005–2014) monthly average energy data of global horizontal irradiation (GHI), direct normal
irradiation (DNI) and temperature (T ), as obtained from the PVGIS-5 geo-temporal irradiation
database (PVGIS 2020).
Table 1 . Ten years (2005–2014) monthly average GHI, DNI and temperature (T ) for Kolkata.
Month Unit area energy content of GHI (kWh/m2) Unit area energy content of DNI (kWh/m2) T (°C)
January 138.669 157.515 17.68
February 150.763 162.24 21.8
March 190.778 176.104 27.02
April 200.373 167.353 30.33
May 191.853 134.267 30.98
June 150.526 84.886 29.64
July 142.831 72.639 27.6
August 139.621 73.557 27.49
September 138.162 90.139 27.05
October 142.856 123.482 25.62
November 138.573 148.147 21.99
December 133.692 157.11 17.94
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 31
Figures 1 and 2 depict the monthly hourly average GHI and diffuse horizontal irradiation (DHI)
(in W/m2) for Kolkata for the year 2014 from 7:30 am to 5:30 pm, as obtained from the NREL data-
base and Figure 3 provides the monthly DHI to GHI ratio for the period of 2005–2015, as obtained
from the PVGIS-5 geo-temporal irradiation database (PVGIS 2020).
Figure 1. Monthly hourly average GHI in Kolkata.
Figure 3. Monthly DHI to GHI ratio for Kolkata for the period of 2005–2015 .
Figure 2. Monthly hourly average DHI in Kolkata.
32 S. BHATTACHARYA ET AL.
2.2. The Perez model
The Perez model was proposed in 1990 and it can be used to model daylight availability from direct
and global solar irradiance data (Perez et al. 1990). It has been applied and validated by researchers
for estimating daylight availability for different climatic conditions (Muneer and Angus 1993;
Muneer 1996; Joshi, Sawhney, and Buddhi 2007; Patil, Garg, and Kaushik 2013; Raul, Pal, and
Roy 2015). Solar irradiance, dew point temperature and geographical altitude data are prerequisites
of this model to compute daylight availability of any geographical region of known latitude and
longitude. The calculation procedure is described below.
The global (Kg) and diffuse (Kd) luminous efficacies can be found out from the equation given
herein
Kg or Kd = ai + biW + ci cos (z)+ di loge (D) . . . (1)
where ai, bi, ci and di are coefficients dependent upon sky clearness and are given in Table 2, W is
atmospheric precipitable water content, z is the zenith angle and D is sky brightness.
The atmospheric precipitable water content (W) is given as
W = e(0.07Td−0.075) . . .(2)
where Td is the dew point temperature and W is given in cm (Wright, Perez, and Michalsky 1989).
Solar zenith angle (z) is calculated through the following equation
cos (z) = cos (w)∗ cos (d)∗ cos (v)+ sin (w)∗sin(d) . . . (3)
where w is the latitude of the geographical location, d is the solar declination angle and v is the hour
angle.
Solar declination angle (d) may be calculated as
d = 23.45◦ sin
360(284+ n)
365
( )
. . . (4)
where n is the date serial number of the year.
Hour angle (v) is calculated from the following formula
v = 15◦(LST− 12) . . . (5)
LST is the local solar time, dependent upon local time (LT) and is given as follows
LST = LT+ 4(Longitude− 15◦∗DTUTC)+ 9.87 sin (2B)− 7.53 cos (B)− 1.5sin(B)
60
. . . (6)
Table 2 . Global and diffuse luminous efficacy coefficients of Perez model.
Sky category
Sky clearness (1)
Coefficients of global luminous
efficacy (Kg)
Coefficients of diffuse luminous
efficacy (Kd)
Lower limit Upper limit ai bi ci di ai bi ci di
Overcast 1 1.065 96.63 −0.47 11.5 −9.16 97.24 −0.46 12 −8.91
1.065 1.23 107.54 0.79 1.79 −1.19 107.22 1.15 0.59 −3.95
1.23 1.5 98.73 0.7 4.4 −6.95 104.97 2.96 −5.53 −8.77
1.5 1.95 92.72 0.56 8.36 −8.31 102.39 5.59 −13.95 −13.9
1.95 2.8 86.73 0.98 7.1 −10.94 100.71 5.94 −22.75 −23.74
2.8 4.5 88.34 1.39 6.06 −7.6 106.42 3.83 −36.15 −28.83
4.5 6.2 78.63 1.47 4.93 −11.37 141.88 1.9 −53.24 −14.03
Clear 6.2 – 99.65 1.86 −4.46 −3.15 152.23 0.35 −45.27 −7.98
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 33
B is expressed in degrees and is given as
B = 360
365
(n− 81) . . . (7)
DTUTC is the difference of the LT from Universal Coordinated Time in hours.
Sky clearness (1) is expressed as
1 = ((Id + In)/Id)+ kz3
1+ kz3
. . . (8)
where Id is the diffuse irradiance, In is the normal irradiance, k = 1.041 and z is the solar zenith angle
in radians.
Normal irradiance (In) is expressed as
In = Ib
cos(z)
. . . (9)
where Ib is the beam irradiance or direct normal irradiance (DNI).
Sky brightness (D) is expressed as
D = m
Id
IE
. . . (10)
where m is the air mass and IE is the horizontal extra-terrestrial irradiance.
Air mass m is obtained from the following formula (Kasten 1993)
m = [sin (as)+ 0.50572(as + 6.07995)−1.6364]−1 . . . (11)
where as is the solar altitude angle and it is complementary to the solar zenith angle z.
Corrected air mass (m’), taking into account the effects of air pressure, is given by
m′ = m∗ p
p0
. . . (12)
Standard atmospheric pressure p0 is 1013.25 millibar at sea level. Atmospheric pressure p (millibar)
at any height h may be determined by the following equation
p = p0e
−0.0001184h . . . (13)
The horizontal extra-terrestrial irradiance (IE) is calculated as (Kalogirou 2014)
IE = GSC 1+ 0.033 cos
360n
365
( )[ ]
cos (z) . . . (14)
where GSC is the solar constant having a value of 1366.1 W/m2 as per the latest adopted value of the
American Society for Testing and Materials (ASTM).
Two parameters to quantify solar or more specifically daylight illumination, namely, global hori-
zontal illuminance (Eg) and diffuse horizontal illuminance (Ed), are determined from the following
two equations:
Eg = Ig∗Kg . . . (15)
Ed = Id∗Kd . . . (16)
where Ig and Id are mathematical notations used to denote GHI and DHI, respectively. Diffuse hori-
zontal illuminance (Ed) values cannot exceed global horizontal illuminance (Eg) and as such, Ed
values marginally exceeding Eg values, if any, should be equated to the corresponding Eg values
to maintain consistency of the model.
34 S. BHATTACHARYA ET AL.
2.3. The IESNA recommended calculation procedure of estimating daylight availability
The IESNA recommended calculation procedure of daylight availability was first published in 1984
(IES Calculation Procedures Committee 1984). It was an amelioration over earlier publications
regarding this subject and it included partly cloudy sky conditions. The elementary calculation pro-
cedure is given herein.
Solar declination angle (d) is given by the following equation
d = 0.4093 sin
2p
368
(n− 81)
[ ]
. . . (17)
where n is the day of the year (1 ≤ n ≤ 365).
Solar altitude angle (as) may be determined from the following equation
as = arcsin sin l∗ sin d− cos l∗ cos d∗ cospt
12
[ ]
. . . (18)
where l is the latitude of the geographical location and t is the solar time.
The solar azimuth angle (αaz) can be found out from the following expression
aaz = arctan
cos d sin
pt
12
cos l sin d+ sin l cos d cos
pt
12
⎡
⎢⎣
⎤
⎥⎦ . . . (19)
The extra-terrestrial solar illuminance Ext is determined from the following equation
Ext = ESC 1+ 0.034 cos
2p
365
(n− 2)
{ }[ ]
. . . (20)
where ESC is the solar illumination constant at 127.5 klx.
The direct normal solar illuminance Edn is governed by the following equation
Edn = Ext∗e−c∗m . . . (21)
where c is the atmospheric extinction coefficient as given in Table 3 andm is the optical air mass as
given in the following equation
m = 1
sinas
. . . (22)
The direct horizontal solar illuminance EdH is found out from the following equation
EdH = Edn sinas . . . (23)
The horizontal sky illuminance EkH, which can be thought to be analogous to the diffuse horizontal
illuminance (Ed) term of Perez model, can be estimated from the given equation herein
EkH = A+ BsinC(as) . . . (24)
Table 3 . Various IESNA recommended daylight availability constants.
Sky
condition
Atmospheric extinction
coefficient (c)
Sunrise to sunset
illuminance ratio (A) in klx
Solar Altitude Illuminance
Coefficient (B) in klx
Solar altitude illuminance
exponent (C ) in klx
Clear 0.21 0.8 15.5 0.5
Partly
cloudy
0.80 0.3 45 1
Cloudy a 0.3 21 1
aFor no direct sun, Edn = 0.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 35
where A is the ratio of sunrise to sunset illuminance, B and C are solar altitude illuminance coeffi-
cient and solar altitude illuminance exponent, respectively, and are given in Table 3.
Now, the sky condition is divided into three types: clear, partly cloudy and cloudy. The concept
of sky ratio was utilised which could take account of the atmospheric scattering of incident solar
irradiation by gases, dust, water vapour and colloidal particles. Sky ratio is the ratio of diffuse hori-
zontal (sky) irradiance to global horizontal irradiance. Table 4 provides the sky ratio ranges corre-
sponding to the three sky conditions.
Total daylight illuminance (E), which can be thought to be analogous to the global horizontal
illuminance (Eg) term of Perez model, is the summation of direct horizontal solar illuminance
and horizontal sky (diffuse) illuminance.
E = EdH + EkH . . . (25)
2.4. Measurement of solar illuminance
To validate the simulated results, solar illuminance was measured in the month of April 2020 in
Kolkata, India with an unobstructed sky. The measurements were conducted every day from
7:30 am to 5:30 pm with an interval of one hour. Two digital photometers were utilised to carry
out the measurements. One measured the global solar illuminance component on a surface parallel
to the ground for the unabated sky dome, the other was provided with a custom-made manually
operable shading ring-shaped apparatus to measure the diffuse solar illuminance on the same sur-
face. The measured values were duly tabulated.
2.5. Estimation of indoor daylight illuminance with window shading control
The daylight transmitted through windows with installed shading devices is usually governed by the
position of the sun and the prevailing conditions of the sky (Molina, Maestre, and Lindawer 2000).
Control strategies and connections to electrical lighting systems of automatically controlled blinds
influence electrical energy savings (Konstantoglou and Tsangrassoulis 2016). Yao (2018) developed
a stochastic model to simulate daylighting with manual solar shades and found that manual solar
shades could significantly increase useful daylight illuminance. A detailed methodology to calculate
indoor daylight levels for office spaces with motorised blinds was provided by Athienitis andTzem-
pelikos (2002). The fundamental equations to calculate daylight transmittance under both clear and
overcast sky conditions are given herein.
The transmittance for the overcast sky is given by the following equation
tovercastn = 4.5∗1012∗b−6
e(335/b) − 1
. . . (26)
where β is the blind tilt angle.
The transmittance for the clear sky is given by the following equation
tclearn (b, u) = 0.55e−(b−80)2/1900 × (− 4.917× 10−7u4 + 0.00009u3 − 0.00567u2 + 0.13u− 0.00437)
(27)
where θ is the angle of incidence of direct sunlight on window glazing or surface.
Table 4 . Sky ratio ranges corresponding to different sky
conditions.
Sky condition Sky ratio
Clear <0.3
Partly cloudy 0.3–0.8
Cloudy >0.8
36 S. BHATTACHARYA ET AL.
For an overcast sky, the total daylight incident upon awindow is the summation of daylight reach-
ing from the sky and from the ground after reflection. This may be mathematically expressed as
Eovercastw = Eh∗Fw−sky + Eh∗Fw−g∗rg . . . (28)
where Ew is the daylight incident upon a window, Eh is analogous to global horizontal illuminance or
total daylight illuminance, Fw-sky is the view factor between the window and the sky (typical value
0.5), Fw-g is the view factor between the window and the ground (typical value 0.5) and rg is the
reflectance of the ground.
The daylight transmitted into the room on an overcast day may be given by the following
equation
Eovercast = Eh(Fw−sky + Fw−g∗rg)∗tovercastn . . . (29)
For a clear sky, the total daylight incident upon a window is the summation of daylight directly from
the sun, diffuse skylight and daylight reflected from the ground. This may be mathematically
expressed as
Eclearw = Ew−sun + Fw−sky∗Eh−sky + Eh∗Fw−g∗rg . . . (30)
where Eh−sky is the horizontal sky (diffuse) component of daylight illuminance.
The term Ew−sun is given by the following equation
Ewsun = E0 × f (n)× e−c×m × cos (u) . . . (31)
where E0 is the average illuminance upon a surface, outside the atmosphere of earth, that normal to
incident sunlight and its value is taken as 127,500 lx. The superscript ‘c’ is given in Table 3 and ‘m’ is
given in Equation (22). The term f(n) is a correction factor that takes account of the orbital shape of
the earth (ellipse). It is given by the following equation (n is day serial number)
f (n) = 1+ 0.033 cos
360n
365
( )
. . . (32)
The daylight transmitted into the room on a clear day may be given by the following equation
Eclear = Eclearw × tclearn . . . (33)
2.6. Estimation of indoor daylight illuminance with light pipes
Indoor daylight illumination can be achieved with the assistance of light pipes which are straight
cylinders with highly reflective inner surfaces. Light pipes may help daylight penetrate into deeper
spaces of buildings. Various studies have analysed the daylighting and ESP of light pipes under
different climate conditions (Carter 2002; Yun, Hwang, and Kim 2010; Samuhatananon, Chirarat-
tananon, and Chirarattananon 2011; Shin, Yun, and Kim 2012). A study (Mandal and Roy 2016)
proposed an adaptive dimming scheme for a light pipe-integrated indoor lighting system that
could maintain a constant workplane illuminance level. The flux confinement diagram (FCD) of
rectangular light pipes was developed by Van Derlofske and Hough (2004) which could assist in
designing first-order light pipe luminous flux transmission systems. However, a simplified method
to determine average indoor daylight illumination for a room with ceiling mounted light pipes was
provided by Maňková, Hraška, and Janak (2009) which is useful to predict indoor daylight illumi-
nance contribution from light pipes using computed exterior daylight illuminance data. The
equation for average horizontal workplane illuminance calculation is given herein
Eavg =
n× Eg × Ad × ht × UF× LLF
A
. . . (34)
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 37
where n is the number of light pipes, Eg is the global horizontal illuminance, Ad is the area of diffu-
ser, ht is the tube transmission efficacy, UF is the utilisation factor, LLF is the light loss factor and A
is the workplane area. Equation (34) is best suited for application with CIE standard overcast sky
but it may also be applied to calculate indoor daylight illuminance for other prevailing sky con-
ditions as well with a reasonable degree of accuracy.
2.7. Estimation of indoor daylight illuminance with skylights
Ceiling mounted skylights are a viable alternative to light pipes to introduce daylight into indoor
environments. These can be also used in conjunction with light pipes. Skylights can save the elec-
trical energy required for lighting and heating and provide a ‘natural view’ of the sky. Different
studies have explored the visual and non-visual effects of the artificial skylight (Canazei et al.
2016; Canazei et al. 2017; Yasukouchi et al. 2019) and it was found that artificial skylights elevate
occupants’ mood in windowless environments (Canazei et al. 2017). The ‘lumen method of sky-
lighting’ to estimate average horizontal workplane illuminance due to skylights was elucidated by
Kaufman (1981). The average horizontal workplane illuminance due to a number of skylights
can be determined from the following equation for an overcast sky (Murdoch 1994)
Eovercastavg = Eh ×
N∗Askylight
Aworkplane
× Kdiffuse × Km . . . (35)
For a day with clear sky and direct sunlight, the same can be given as follows
Eclearavg = Ehsky
N × Askylight
Aworkplane
( )
× Kusky × Km + Ehsun
N × Askylight
Aworkplane
( )
× Kusun × Km . . . (36)
where N is the number of skylights, Kdiffuse is the coefficient of utilisation for overcast sky
component, Kusky is the coefficient of utilisation for clear sky component, Kusun is the coefficient
of utilisation for direct sun component, Km is the light loss factor, Askylight is the gross area of
each skylight and Aworkplane is the gross area of the indoor horizontal workplane.
2.8. Estimation of indoor daylight illuminance with light shelves
A light shelf is a simple horizontal or very nearly horizontal baffle that is fitted higher up a window
to achieve a degree of control over the distribution of incident luminous flux from the sky and the
sun (Littlefair 1995). Light shelves can facilitate daylight penetration into deeper indoor spaces and
increase the useful daylight illuminance (Kontadakis et al. 2018). Different studies have explored
how light shelves can be used for daylighting, their effectiveness, performance and ESP (Soler
and Oteiza 1997; Binarti and Dewi 2016; Lee et al. 2017; Solovyov 2017; Bahdad, Fadzil, and
Taib 2020; Zazzini et al., 2020). Tsangrassoulis et al. (2019) provided a methodology based on radio-
metric quantities to model the performance of a light shelf. That methodology can be utilised to
evaluate the daylighting performance of light shelves by considering analogous photometric quan-
tities. It is deliberated briefly herein:
The luminous flux that is reflected from the exterior light shelf and incident upon the clerestory
(upper window) due to DNI is given by
fbeam = Eg − Ed
cos (z)
× rshelf × Areflected × cos (u2) . . . (37)
where Eg is the global horizontal illuminance, Ed is the diffuse horizontal illuminance, z is the solar
zenith angle, ρshelf is the reflectance of the light shelf, Areflected is the area linkage of the clerestory
(upper window) with the reflected beam of sunlight and θ2 is the angle of incidence of the reflected
38 S. BHATTACHARYA ET AL.
beam of sunlight with the window plane and it is equal to the solar zenith angle z when the light
shelf is a horizontal surface.
The luminous flux that is reflected from the exterior light shelf and incident upon the clerestory
(upper window) due to diffuse sky irradiation is given by
wdiffuse = Ed × rshelf × Fw−shelf × Fsky−shelf × Auw . . . (38)
where Ed is the diffuse horizontal illuminance, Fw-shelf is the view factor of the clerestory (upper win-
dow) to the light shelf, Fsky-shelf is the view factor of the light shelf to the sky and Auw is the surface
areaof the clerestory (upper window).
Thus, the daylight transmitted into the room through the clerestory (upper window) due to the
externally placed horizontal light shelf is given by
Etransmitted = wbeam + wdiffuse
Auw
× tv . . . (39)
where τν is the transmittance of the window glazing. Equation (39) can be utilised to assess the day-
light contribution by the upper reflective surface of an externally placed light shelf for different glaz-
ing transmittance values.
3. Results
3.1. Estimations from the Perez model
3.1.1. Average global and diffuse luminous efficacy
The estimated average global and diffuse luminous efficacy values are provided in Tables 5 and 6,
respectively. It can be seen from Table 5 that the monthly average global luminous efficacy values of
the months from June to September are usually higher than other months and June has the highest
monthly average global efficacy value. June to August are usually the months of monsoon in Kolkata
and September is the beginning of autumn, albeit with considerable cloud covering. The monthly
average global luminous efficacy values for June, July, August and September are 111.52, 110.84,
110.15 and 110.38 lm/W, respectively. The lowest monthly average global luminous efficacy values
are obtained for the months of December, January and February, those are 98.58, 97.14 and
98.74 lm/W, respectively. The estimated yearly average global luminous efficacy is 105.00 lm/W.
From Table 6, it may be ascertained that the monthly average diffuse luminous efficacy values of
the months from June to September are usually higher than other months and August has the high-
est monthly average diffuse efficacy value. Overcast conditions mostly prevail during these months
and most of the time, the sky has a considerable cloud covering, which contributes to the increment
of diffuse luminous flux in the atmosphere. The monthly average diffuse luminous efficacy values
for June, July, August and September are 140.04, 140.23, 142.13 and 139.61, respectively. The lowest
Table 5 . Average global luminous efficacy (lm/W) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 76.14 85.65 94.42 99.91 102.93 102.53 102.26 101.10 98.97 107.76 96.90
Feb 79.03 90.56 97.07 100.99 103.31 104.25 104.12 103.07 102.07 103.47 98.24
Mar 85.46 94.63 100.16 103.64 105.61 106.19 105.86 104.70 102.61 101.53 126.75
Apr 87.72 97.39 102.88 105.70 107.32 107.91 107.44 105.89 105.59 105.60 116.90
May 94.56 102.79 108.57 112.21 114.42 114.63 113.71 112.07 109.48 105.41 113.60
Jun 99.62 105.51 110.17 113.24 114.24 115.08 116.15 114.53 112.94 110.50 114.77
Jul 98.05 104.54 108.29 111.84 113.94 116.03 116.08 115.00 112.50 109.88 113.11
Aug 92.63 101.95 108.44 112.66 114.18 115.88 114.54 113.83 111.34 110.84 115.42
Sep 96.13 101.35 107.79 110.96 114.20 114.71 113.84 111.71 109.76 108.24 125.52
Oct 91.31 97.38 104.66 107.72 110.35 110.04 110.04 108.32 105.49 111.91 95.30
Nov 84.11 91.31 98.79 103.03 104.16 104.60 105.20 102.99 99.84 118.91 95.34
Dec 78.66 90.16 95.12 99.86 101.64 102.45 101.82 101.57 99.08 117.94 96.05
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 39
monthly average diffuse luminous efficacy values are obtained for the months of December, January
and February, those are 106.36, 105.07 and 106.91 lm/W, respectively. The estimated yearly average
diffuse luminous efficacy is 123.92 lm/W.
3.1.2. Average global and diffuse horizontal illuminance
The estimated average global and diffuse horizontal illuminance values are provided in Tables 7 and
8, respectively. From Table 7, it can be found out that the months of April and May record the high-
est monthly average global horizontal illuminance at 61.90 and 58.87 klx, respectively, as these
months are the summer months in Kolkata and receive more solar insolation than other months.
The winter months of December and January find the lowest monthly average global horizontal
illuminance at 34.82 and 37.65 klx, respectively, due to the tilt of the earth’s axis. The estimated
yearly average global horizontal illuminance is 47.71 klx.
From Table 8, it is seen that the months of June and July record the highest monthly average
diffuse horizontal illuminance at 35.32 and 35.29 klx, respectively, which is due to the overcast con-
ditions of the monsoon season. The winter months of December and January, as it was the case for
global horizontal illuminance values, record the lowest monthly diffuse horizontal illuminance at
18.84 and 20.10 klx, respectively. The estimated yearly average diffuse horizontal illuminance is
27.00 klx.
3.2. Estimations from the IESNA recommended calculation procedure
3.2.1. Direct horizontal solar illuminance and horizontal sky illuminance
The estimated average direct horizontal solar illuminance and horizontal sky illuminance values are
provided in Tables 9 and 10 respectively. From Table 9, it can be concluded that the months of May
Table 6. Average diffuse luminous efficacy (lm/W) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 52.98 94.15 105.60 112.00 114.98 115.28 115.88 116.40 114.39 116.60 97.57
Feb 61.81 92.82 106.40 112.53 115.67 116.95 117.84 118.71 117.52 117.05 98.77
Mar 79.87 102.01 111.85 116.35 118.26 118.45 118.87 119.72 120.23 121.48 126.73
Apr 102.88 113.12 115.63 118.35 119.58 120.45 120.88 120.68 122.36 118.96 117.18
May 124.33 132.76 137.53 139.72 141.06 140.46 139.67 139.68 139.71 138.53 118.58
Jun 132.85 137.10 139.66 141.07 147.08 147.72 142.85 142.65 143.95 145.14 120.35
Jul 129.40 134.78 142.50 145.25 146.27 141.97 142.69 143.09 142.36 142.89 131.34
Aug 124.03 136.44 143.36 147.13 146.89 141.48 147.25 148.93 148.82 144.79 134.33
Sep 110.33 134.90 142.02 134.69 147.51 147.63 147.17 146.20 147.66 152.15 125.50
Oct 96.47 124.20 124.35 128.40 138.15 136.81 138.44 138.69 139.06 127.99 96.00
Nov 76.15 107.57 116.95 121.49 118.94 120.07 124.85 124.01 123.78 119.10 96.00
Dec 60.89 92.56 107.29 112.45 114.45 116.43 116.34 117.06 117.64 118.16 96.70
Table 7. Average global horizontal illuminance (klx) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 10.82 27.29 44.55 60.08 67.19 65.92 60.46 45.35 26.30 6.20 0
Feb 14.41 36.09 56.62 71.47 78.09 74.96 66.35 53.25 33.18 12.20 0.02
Mar 24.40 48.04 69.99 86.23 94.42 91.89 80.34 61.43 40.37 16.72 0.49
Apr 32.66 56.28 80.00 94.33 100.76 98.56 85.50 66.64 44.96 19.50 1.79
May 34.80 59.00 75.21 86.23 88.74 89.73 79.88 64.96 43.55 21.65 3.85
Jun 28.37 46.21 61.55 72.90 80.04 73.76 59.24 48.96 32.59 17.76 4.43
Jul 28.90 47.60 60.72 74.21 77.13 67.77 58.26 46.94 33.72 22.47 6.32
Aug 25.89 47.70 69.30 79.11 81.89 69.52 65.84 52.46 41.21 16.24 3.28
Sep 30.18 49.90 69.60 81.56 82.29 71.02 61.51 45.81 29.62 11.30 0.36
Oct 26.19 46.97 65.89 76.94 76.33 67.03 53.96 40.03 21.77 5.22 0
Nov 20.39 39.07 57.76 70.03 73.54 68.70 56.77 39.01 18.37 1.08 0
Dec 13.15 31.41 46.77 59.92 64.03 60.81 51.84 36.42 17.41 1.27 0
40 S. BHATTACHARYA ET AL.
to July receive the highest monthly average direct horizontal solar illuminance at 33.81, 32.84 and
34.09 klx, respectively. November to January record the lowest monthly average direct horizontal
solar illuminance at 15.70, 13.17 and 14.42 klx, respectively.
From Table 10 it can be calculated that the months of May to August record the highest monthly
average horizontal sky (diffuse) illuminance at 32.30, 31.48, 32.49 and 31.48 klx, respectively, while
the months of December and January record the lowest at 19.53 and 20.39 klx, respectively.
3.2.2. Total daylight illuminance
Total daylight illuminance is the summation of direct horizontal solar illuminance and horizontal
sky (diffuse) illuminance and Table 11 provides the average total daylight illuminance. It can be
seen that the months of May to August receive the highest monthly average total daylight illumi-
nance at 66.11, 64.32, 66.58 and 63.82 klx, respectively, while December and January receive the
lowest monthly average total daylight illuminanceat 32.70 and 34.81 klx, respectively.
3.3. Measured solar illuminance and comparison with computed illuminance levels
Solar illuminance, more specifically the global and diffuse horizontal parts of it, were measured with
two digital photometers in April 2020 in Kolkata. Figure 4 compares the measured values with the
computed values for the month of April, obtained from the application of Perez model and IESNA
recommended calculation procedure, and shows percentage error bars accounting for error values
up to 5% for reference.
It can be inferred that the values calculated from Perez model are much closer to the measured
values than the values calculated from the IESNA recommended calculation procedure. This may
be attributed to the simplistic three condition-based sky classification (clear, partly cloudy and
Table 8. Average diffuse horizontal illuminance (klx) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 5.97 17.87 25.43 29.71 32.01 30.82 29.01 24.69 19.35 6.20 0
Feb 8.19 19.74 26.31 30.36 32.15 32.55 30.78 26.8 21.67 11.5 0.02
Mar 14.16 23.43 28.86 32.15 33.96 34.82 33.36 29.54 23.53 14.32 0.49
Apr 22.33 30.1 32.74 35.44 36.62 36.19 34.49 31.51 26.37 16.38 1.79
May 26.08 33.63 37.51 40.08 40.73 41.76 40.44 35.75 28.57 20.35 3.85
Jun 27.42 38.76 44.16 48.07 49.76 48.97 41.96 38.32 28.9 17.76 4.43
Jul 26.49 37.81 41.73 47.21 49.58 47.07 42.88 37.23 31.04 20.79 6.32
Aug 22.77 33.21 38.73 42.17 46.9 45.82 44.63 36.96 29.27 15.86 3.28
Sep 17.75 29.67 35.97 36.31 41.32 41.61 39.61 34.91 24.61 11.30 0.36
Oct 16.81 27.93 31.19 34.36 39.59 41.35 35.46 28.65 19.91 5.22 0
Nov 12.62 24.08 29.91 34.06 33.98 33.35 31.53 25.8 17.46 1.08 0
Dec 7.64 18.2 25.78 30.08 29.92 29.14 27.35 22.77 15.06 1.27 0
Table 9. Average direct horizontal solar illuminance (klx) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 0 2.26 10.99 21.23 28.86 31.79 29.37 22.14 12.01 0 0
Feb 0.01 3.91 14.91 26.95 35.88 39.64 37.47 29.81 18.31 0 0
Mar 0.57 8.90 22.89 36.33 45.73 49.32 46.47 37.67 24.56 10.38 0
Apr 3.40 15.87 31.33 44.84 53.66 56.38 52.51 42.72 28.62 13.25 0
May 6.27 20.08 35.51 48.48 56.67 58.89 54.75 44.92 30.88 15.41 0
Jun 6.60 20.26 35.47 48.37 56.69 59.20 55.58 46.31 32.78 0 0
Jul 5.20 18.39 33.78 47.16 56.06 59.18 56.04 47.12 33.73 18.34 0
Aug 3.66 16.28 31.84 45.54 54.65 57.78 54.35 44.99 31.14 15.59 0
Sep 2.46 13.97 28.86 41.87 50.17 52.30 47.91 37.74 23.65 9.18 0
Oct 1.19 10.28 23.28 34.71 41.63 42.67 37.64 27.50 14.57 0 0
Nov 0.22 5.87 16.42 26.37 32.52 33.42 28.88 19.92 9.07 0 0
Dec 0.01 2.95 11.67 21.08 27.53 29.25 25.84 18.13 8.42 0 0
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 41
cloudy) part in the IESNA recommended calculation procedure which is perceived as a weakness of
this procedure (Kandilli and Ulgen 2008). Kittler, Perez, and Darula (1997) proposed a sky classifi-
cation method with 15 sky categories based on sky conditions and it was accepted by the CIE as CIE
standard general sky (CIE 2003). The IESNA recommended calculation procedure may yield better
results if it adopts, in a future modification of its original publication, the CIE standard general sky
classification method to classify sky types.
3.4. Estimations from window shading control simulation
The daylight illuminance transmitted into a room through a window having shading control was
calculated in accordance with the methodology proposed by Athienitis and Tzempelikos (2002).
External horizontal daylight data obtained from application of Perez model and IESNA rec-
ommended calculation procedure were utilised. Calculations were conducted for blind tilt angle
(β) values of 30◦, 45◦ and 60◦ assuming an eastward window with Fw-g = 0.5, Fw-sky = 0.5 and
rg = 0.2. Equation (26) was utilised to simplify the glazing transmittance calculations as Equation
(27) can only be applied when there is incidence of direct sunlight upon window glazing and win-
dows facing a particular direction receive direct sunlight for a fraction of sunshine duration only.
Figure 5 demonstrates the estimated daylight illuminance transmitted into a room through an east-
ward window with motorised shading control for data obtained from Perez model and Figure 6
demonstrates the same for data obtained from IESNA recommended calculation procedure. For
β = 30◦, the highest and lowest monthly average daylight illuminance transmission through an
eastward window with motorised shading control were 4055 lx (July) and 2679 lx (December)
respectively for Perez model, and 4098 lx (July) and 2691 lx (December) respectively for IESNA rec-
ommended calculation procedure. For β = 45◦, the highest and lowest monthly average daylight
illuminance transmission through an eastward window with motorised shading control were
Table 10. Average horizontal sky (diffuse) illuminance (klx) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 3.72 13.13 21.25 27.53 31.53 32.98 31.79 28.03 21.96 6.69 5.64
Feb 5.38 15.26 23.86 30.58 34.97 36.73 35.72 32.02 25.9 8.45 3.97
Mar 9.7 19.76 28.45 35.18 39.49 41.09 39.82 35.81 29.34 20.85 5.25
Apr 14.66 24.43 32.75 39.06 42.94 44.12 42.44 38.11 31.4 22.78 6.14
May 17.56 26.87 34.76 40.67 44.22 45.16 43.4 39.09 32.52 24.13 6.93
Jun 17.84 26.97 34.74 40.62 44.22 45.29 43.75 39.71 33.45 12.01 7.66
Jul 16.56 25.91 33.93 40.09 43.95 45.28 43.95 40.07 33.91 25.88 7.87
Aug 14.96 24.67 33 39.37 43.36 44.71 43.23 39.13 32.66 24.25 6.92
Sep 13.46 23.26 31.54 37.74 41.45 42.38 40.45 35.83 28.85 19.96 4.72
Oct 11.31 20.79 28.67 34.42 37.66 38.13 35.81 30.87 23.65 7 5.52
Nov 8.14 17.22 24.77 30.27 33.35 33.79 31.55 26.8 19.87 5.39 3.26
Dec 5.02 14.07 21.71 27.43 30.84 31.71 29.98 25.76 19.34 5.37 3.62
Table 11. Total average daylight illuminance (klx) in Kolkata.
Month/hour 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30
Jan 3.72 15.39 32.24 48.76 60.39 64.77 61.16 50.17 33.97 6.69 5.64
Feb 5.39 19.17 38.77 57.53 70.86 76.37 73.19 61.83 44.21 8.45 3.97
Mar 10.27 28.66 51.34 71.50 85.23 90.42 86.29 73.48 53.90 31.23 5.25
Apr 18.05 40.30 64.09 83.91 96.60 100.50 94.95 80.83 60.02 36.02 6.14
May 23.83 46.96 70.26 89.15 100.89 104.05 98.15 84.01 63.39 39.54 6.93
Jun 24.44 47.22 70.21 89.00 100.91 104.49 99.33 86.02 66.22 12.01 7.66
Jul 21.77 44.30 67.71 87.25 100.02 104.46 99.99 87.19 67.64 44.22 7.87
Aug 18.61 40.96 64.84 84.91 98.00 102.49 97.58 84.12 63.80 39.84 6.92
Sep 15.92 37.23 60.40 79.61 91.61 94.69 88.36 73.57 52.49 29.14 4.72
Oct 12.50 31.07 51.95 69.13 79.29 80.81 73.45 58.37 38.22 7.00 5.52
Nov 8.36 23.09 41.20 56.64 65.87 67.21 60.43 46.72 28.94 5.39 3.26
Dec 5.03 17.02 33.38 48.51 58.37 60.96 55.82 43.89 27.77 5.37 3.62
42 S. BHATTACHARYA ET AL.
14730 lx (July) and 9732 lx (December), respectively, for Perez model, and 14,888 lx (July) and
9776 lx (December), respectively, for IESNA recommended calculation procedure. For β = 60◦,
the highest and lowest monthly average daylight illuminance transmission through an eastward
window with motorised shading control were 16,913 lx (July) and 11,175 lx (December) respectively
for Perez model, and 17,094 lx (July) and 11,224 lx (December), respectively, for IESNA rec-
ommended calculation procedure.
3.5. Estimations from light pipe simulation
Indoor average horizontal workplane illuminance levels were computed using the methodology as
deliberated by Mandal and Roy (2009). Calculations were conducted with Equation (34) for four
light pipes (n=4) and six light pipes (n = 6) respectively assuming Ad = 0.2, ht = 0.75, UF = 0.8,
LLF = 0.5 and A = 60. Figure 7 demonstrates the estimated average horizontal workplane illumi-
nance levels for data obtained from Perez model and Figure 8 demonstrates the same for data
obtained from IESNA recommended calculation procedure. For four light pipes (n = 4), the highest
and lowest monthly average horizontal workplane illuminance levels were 248 lx (April) and 139 lx
(December), respectively, for Perez model, and 266 lx (July)and 131 lx (December), respectively,
for IESNA recommended calculation procedure. For six light pipes (n = 6), the highest and lowest
monthly average horizontal workplane illuminance levels were 371 lx (April) and 209 lx (Decem-
ber), respectively, for Perez model, and 400 lx (July) and 196 lx (December), respectively, for
IESNA recommended calculation procedure.
3.6. Estimations from skylight simulation
Equations (35) and (36) were used, in accordance with the procedure propounded byMurdoch (1994),
to calculate indoor average horizontal workplane illuminance levels for overcast sky and clear sky,
respectively. Calculations were conducted for two skylights (N = 2) and four skylights (N = 4) respect-
ively assuming As = 0.8, Kusky = 0.75, Kusun = 0.8, Km = 0.55, Kdiffuse = 0.75 and A = 60 m2.
Figure 9 demonstrates the estimated average horizontal workplane illuminance levels for data
obtained from Perez model and Figure 10 demonstrates the same for data obtained from IESNA
recommended calculation procedure. For two skylights (N = 2) with clear sky, the highest and low-
est monthly average horizontal workplane illuminance levels were 706 lx (April) and 395 lx
(December), respectively, for Perez model, and 757 lx (July) and 369 lx (December), respectively,
for IESNA recommended calculation procedure. For two skylights (N = 2) with overcast sky, the
highest and lowest monthly average horizontal workplane illuminance levels were 681 lx (April)
and 383 lx (December), respectively, for Perez model, and 732 lx (July) and 360 lx (December),
respectively, for IESNA recommended calculation procedure. For four skylights (N = 4) with
Figure 4. Comparison of Measured Solar Illuminance Values with Computed Solar Illuminance Values for April Month.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 43
clear sky, the highest and lowest monthly average horizontal workplane illuminance levels were
1412 lx (April) and 789 lx (December), respectively, for Perez model, and 1515 lx (July) and
739 lx (December), respectively, for IESNA recommended calculation procedure. For four skylights
(N = 4) with overcast sky, the highest and lowest monthly average horizontal workplane illumi-
nance levels were 1362 lx (April) and 766 lx (December), respectively, for Perez model, and
1465 lx (July) and 719 lx (December), respectively, for IESNA recommended calculation procedure.
3.7. Estimations from light shelf simulation
The methodology deliberated by Tsangrassoulis et al. (2019) was utilised to introduce a system of
equations (Equations (37–39)) in analogous of photometric quantities which could be used to
Figure 5. Estimation of daylight illuminance transmission into a room through an eastward window with motorised shading
control with data obtained from Perez model.
44 S. BHATTACHARYA ET AL.
calculate the daylight transmitted into a room through a clerestory (upper window) due to an exter-
nally placed horizontal light shelf. Calculations were conducted for ρshelf = 0.8 and 0.9 respectively
assuming Areflected = 0.8 m2, Fw-shelf = 0.50, Fsky-shelf = 0.30, Auw = 2 m2 and τν = 0.75.
Figure 11 demonstrates the estimated daylight illuminance transmitted into a room for data
obtained from Perez model and Figure 12 demonstrates the same for data obtained from IESNA
recommended calculation procedure. For light shelf reflectance of 0.8 (ρshelf = 0.8), the highest
and lowest monthly average daylight illuminance transmission through a clerestory (upper win-
dow) were 10,713 lx (April) and 5531 lx (December), respectively, for Perez model, and 11,106 lx
(July) and 4919 lx (December), respectively, for IESNA recommended calculation procedure. For
light shelf reflectance of 0.9 (ρshelf = 0.9), the highest and lowest monthly average daylight illumi-
nance transmission through a clerestory (upper window) were 12,052 lx (April) and 6223 lx
Figure 6. Estimation of daylight illuminance transmission into a room through an eastward window with motorised shading
control with data obtained from IESNA recommended calculation procedure.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 45
(December), respectively, for Perez model, and 13,043 lx (July) and 5863 lx (December), respect-
ively, for IESNA recommended calculation procedure.
4. Discussion
4.1. Percentage cumulative frequency distribution of solar illuminance and luminous
efficacy parameters
Figures 13 and 14 provide the percentage cumulative frequency distribution curves of global and
diffuse horizontal illuminance for applied Perez model, and total daylight illuminance and horizon-
tal sky (diffuse) illuminance for applied IESNA recommended calculation procedure respectively.
From Figure 13, it can be seen that around 87.88% of the values of global horizontal illuminance
are over 10 klx and for diffuse horizontal illuminance it is nearly 85.61%. From Figure 14, in a simi-
lar observation, it is seen that around 84.10% of the total daylight illuminance values are over 10 klx
and for horizontal sky (diffuse) illuminance it is about 83.34%.
A room is naturally well-lit if the daylight factor (DF) of it is generally over 5%. Assuming a DF of
5%, the extensive availability of external horizontal illuminance levels of 10 klx or more would mean
that an indoor illuminance level of 500 lx can ideally be obtained solely from daylighting. Though this
simplistic assumption may not always provide accurate estimations and the internally reflected com-
ponent, which can be calculated by computational algorithms such as the Monte Carlo method (Roy
and Bandyopadhya 2002), can widely fluctuate due to the temporally variant nature of daylight. In
addition, the mean room surface exitance (MRSE) levels of a daylit indoor space can be estimated
by applying experimental procedures (Cuttle 2010; Duff, Antonutto, and Torres 2016; Duff, Kelly,
and Cuttle 2017; Cuttle 2018). The information about the daytime MRSE levels can be utilised to
Figure 7. Estimation of average horizontal workplane illuminance from light pipe simulation with data obtained from Perez
model.
46 S. BHATTACHARYA ET AL.
estimate the perceived adequacy of illumination (PAI) and target-to-ambient illumination ratio (TAIR)
levels for different hours of a day, and it remains an area of significant current research interest.
Figure 15 provides the percentage cumulative frequency distribution curves of global and diffuse
luminous efficacy for applied Perez model. From Figure 15, it is seen that the global luminous
efficacy is higher than 95 lm/W for around 88.64% of all recorded values and around 87.12% of
the values lie between 95 and 120 lm/W. Similarly, it is seen that the diffuse luminous efficacy is
higher than 110 lm/W for around 84.85% of all recorded values and 84.09% of the values lie
between 110 and 150 lm/W.
4.2. Correlation between solar irradiation and solar illuminance
Figure 16 shows the GHI vs. global horizontal illuminance and DHI vs. diffuse horizontal illumi-
nance scatterplots for the applied Perez model. Pearson correlation coefficient test was utilised to
measure the strength of linear correlation between irradiation and illuminance. The coefficient
of determination (R2) was found to be 0.9866 for the GHI vs. global horizontal illuminance plot,
and 0.9234 for the DHI vs. diffuse horizontal illuminance plot. This implies that there is a large
positive linear association between irradiation and illuminance in the applied Perez model.
Figure 17 shows the GHI vs. total daylight illuminance and DHI vs. horizontal sky illuminance
scatterplots for the applied IESNA recommended calculation procedure. Pearson correlation coeffi-
cient test was once again utilised. The coefficient of determination (R2) was 0.6841 for the GHI vs.
total daylight illuminance plot, and 0.7751 for the DHI vs. horizontal sky illuminance plot. This
implies that the positive linear association between irradiation and illuminance in the applied
IESNA recommended calculation procedure is not as strong as that of the applied Perez model
and applicationof Perez model to Kolkata yielded a better coefficient of determination (R2) values.
Figure 8. Estimation of average horizontal workplane illuminance from light pipe simulation with data obtained from IESNA
recommended calculation procedure.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 47
4.3. Statistical analysis of estimated indoor illuminance levels
The data obtained from global and diffuse horizontal illuminance measurements carried out in
April 2020 in Kolkata, India were further utilised to estimate indoor illuminance levels for various
daylighting methods, in addition to the estimations obtained from the application of the Perez
Figure 9. Estimation of average horizontal workplane illuminance from skylight simulation with data obtained from Perez model.
48 S. BHATTACHARYA ET AL.
model and the IESNA recommended calculation procedure for the same period. Table 12 provides a
compendium of the statistical analysis carried out with April 2020 data using two statistical indi-
cators: root mean square error (RMSE) and mean absolute error (MAE).
Figure 10. Estimation of average horizontal workplane illuminance from skylight simulation with data obtained from IESNA rec-
ommended calculation procedure.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 49
5. Conclusion
This work applied two computational methods of predicting solar illuminance, the Perez model and
the IESNA recommended calculation procedure, to Kolkata to estimate solar illuminance levels on a
horizontal exterior plane, compared the simulated values with those obtained from conducted
measurements and calculated approximate daylight illuminance levels for four daylighting devices,
namely, motorised shading of windows, light pipes, skylights and light shelves. The statistical analy-
sis in Table 12 demonstrates that the indoor daylight level estimations arrived at from the appli-
cation of Perez model for various daylighting devices yield acceptable RMSE and MAE. The
results clearly indicate that the Perez model could be taken as the better choice in predicting global
and diffuse horizontal solar illuminance levels and should be preferred to the IESNA recommended
calculation procedure. In addition, the Perez model could be used to estimate global and diffuse
luminous efficacy values and the application of this model to Kolkata yielded coefficient of deter-
mination (R2) values over 0.92, indicating a large positive linear association between irradiation
and illuminance. From the applied Perez model to Kolkata, the estimated yearly average global
and diffuse luminous efficacy values are 105.00 and 123.92 lm/W, respectively, and the estimated
yearly average global horizontal and diffuse horizontal illuminance values are 47.71 and
27.00 klx, respectively. This is an indicator of the promising daylighting potential of Kolkata
which should be harnessed by architects, construction engineers, building services engineers and
interior designers.
This work may serve as a paradigm in estimating the indoor daylight availability from solar radi-
ation databases in energy-efficient building planning for different geographical locations. Daylight-
ing can reduce electrical energy expenditure in artificial illumination (Mistrick and Sarkar 2005;
Figure 11. Estimation of daylight illuminance transmission into a room through a clerestory from light shelf simulation with data
obtained from Perez model.
50 S. BHATTACHARYA ET AL.
Ihm, Nemri, and Krarti 2009), improve visual comfort and mood (Borisuit et al. 2015; Shishegar
and Boubekri 2016; Asojo, Bae, and Martin 2020), increase employee productivity (Thayer 1995)
and lighting controls responsive to daylight, such as dimmable electronic ballasts (Doulos, Tsan-
grassoulis, and Topalis 2008) can extensively save energy and cut down the financial incurrences.
Daylight influx can be controlled and redistributed in indoor spaces through the use of light shelves
(Beltran, Lee, and Selkowitz 1997; Lim and Heng 2016), light pipes and skylights (Beltran et al.
1994). In addition, shading devices, such as roller shades, prismatic louvres and venetian blinds,
can be utilised to selectively admit daylight and control glare (Nilsson and Jonsson 2010; Eltaweel
et al. 2020), often in tandem with daylight responsive artificial lighting controls. Currently, LEDs
Figure 12. Estimation of daylight illuminance transmission into a room through a clerestory from light shelf simulation with data
obtained from IESNA recommended calculation procedure.
Figure 13. Percentage cumulative frequency curves of global and diffuse horizontal illuminance for applied Perez model.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 51
are viewed as a viable source for general indoor illumination due to the light output, luminous
efficacy and minimal colour shift (Protzman and Houser 2006) and recent works have explored
energy-saving aspects of daylight integration with LED-based indoor illumination systems (Ge
et al. 2014; Doulos et al. 2017). Integration of daylighting systems to already installed artificial
indoor illumination systems is of sustained interest to researchers worldwide and combining it
with renewable energy sources can conserve energy, reduce carbon emission, increase human pro-
ductivity in offices, schools and other institutions, and provide far-reaching benefits to public
health, morale and wellbeing.
Figure 14. Percentage cumulative frequency curves of total daylight illuminance and horizontal sky illuminance for applied
IESNA recommended calculation procedure.
Figure 15. Percentage cumulative frequency curves of global and diffuse luminous efficacy for applied Perez model.
Figure 16. Plots of GHI vs. global horizontal illuminance and DHI vs. diffuse horizontal illuminance for the applied Perez model.
52 S. BHATTACHARYA ET AL.
Acknowledgements
The authors duly credit and express gratitude to the U.S. Department of Energy (DOE)/National Renewable Energy
Laboratory (NREL)/Alliance for Sustainable Energy LLC (ALLIANCE) for providing the 2014 SUNY semi-empirical
model solar irradiance data of Kolkata, India from the National Solar Radiation Database (NSRDB), maintained by
the National Renewable Energy Laboratory (NREL), via e-mail to the corresponding author upon request. The infor-
mation reproduced in this work from the PVGIS-5 geo-temporal irradiation database is authorised by the PVGIS,
European Union, as the source of information is duly cited in the text. The authors would also like to express
Figure 17. Plots of GHI vs. total daylight illuminance and DHI vs. horizontal sky illuminance for the applied IESNA recommended
calculation procedure.
Table 12. Statistical Errors in the Estimation of Indoor Daylight Illuminance Levels.
Daylighting method/
device Matched-pair variables RMSE
%
RMSE MAE
%
MAE
Window shading
control
Estimated daylight illuminance transmission from measurement
data – estimated daylight illuminance transmission from
application of Perez Model
279.82 2.01 266.51 1.91
Estimated daylight illuminance transmission from measurement
data – estimated daylight illuminance transmission from
application of IESNA recommended calculation procedure
379.61 9.91 352.06 9.19
Light Pipe Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of Perez Model
6.86 2.84 6.18 2.56
Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of IESNA recommended
calculation procedure
48.85 20.23 44.14 18.28
Skylight (Overcast
Sky Condition)
Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of Perez model
37.74 2.84 33.98 2.56
Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of IESNA recommended
calculation procedure
268.68 20.23 242.7518.28
Skylight (Clear Sky
Condition)
Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of Perez model
38.27 2.78 34.24 2.48
Estimated average horizontal workplane illuminance from
measurement data – estimated average horizontal workplane
illuminance from application of IESNA recommended
calculation procedure
280.13 20.33 253.36 18.39
Light Shelf Estimated daylight illuminance transmission from measurement
data – estimated daylight illuminance transmission from
application of Perez model
218.72 2.07 172.01 1.63
Estimated daylight illuminance transmission from measurement
data – estimated daylight illuminance transmission from
application of IESNA recommended calculation procedure
2392.71 22.68 2172.13 20.59
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 53
gratefulness to the anonymous referees for offering their expertise to constructively review the paper which assisted
the authors to enhance the quality of the paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Sourin Bhattacharya http://orcid.org/0000-0002-3715-6901
Sudipta Majumder http://orcid.org/0000-0003-1294-1986
References
Al-Ashwal, N. T., and A. S. Hassan. 2017. “Estimation of Energy Savings Due to the Integration of Daylight with
Artificial Lighting in Classrooms.” Advanced Science Letters 23 (7): 6140–6143.
Alrubaih, M. S., M. F. M. Zain, M. A. Alghoul, N. L. N. Ibrahim, M. A. Shameri, and O. Elayeb. 2013. “Research and
Development on Aspects of Daylighting Fundamentals.” Renewable and Sustainable Energy Reviews 21: 494–505.
Asojo, A. O., S. Bae, and C. S. Martin. 2020. “Post-occupancy Evaluation Study of the Impact of Daylighting and
Electric Lighting in the Workplace.” Leukos 16 (3): 239–250.
Athienitis, A. K., and A. Tzempelikos. 2002. “A Methodology for Simulation of Daylight Room Illuminance
Distribution and Light Dimming for a Room with a Controlled Shading Device.” Solar Energy 72 (4): 271–281.
Azad, A. S., D. Rakshit, and K. N. Patil. 2018. “Model Development and Evaluation of Global and Diffuse Luminous
Efficacy for Humid Sub-Tropical Region.” Renewable Energy 119: 375–387.
Bahdad, A. A. S., S. F. S. Fadzil, and N. Taib. 2020. “Optimization of Daylight Performance Based on Controllable
Light-Shelf Parameters Using Genetic Algorithms in the Tropical Climate of Malaysia.” Journal of Daylighting
7 (1): 122–136.
Bellia, L., and F. Fragliasso. 2017. “New Parameters to Evaluate the Capability of a Daylight-Linked Control System in
Complementing Daylight.” Building and Environment 123: 223–242.
Beltran, L. O., E. S. Lee, K. M. Papmichael, and S. E. Selkowitz. 1994. The Design and Evaluation of Three Advanced
Daylighting Systems: Light Shelves, Light Pipes and Skylights (No. LBL-34458; CONF-9406134-4). Berkeley, CA:
Lawrence Berkeley Lab.
Beltran, L. O., E. S. Lee, and S. E. Selkowitz. 1997. “Advanced Optical Daylighting Systems: Light Shelves and Light
Pipes.” Journal of the Illuminating Engineering Society 26 (2): 91–106.
Binarti, F., and S. Dewi. 2016. “The Effectiveness of Light Shelf in Tropical Urban Context.” Environmental and
Climate Technologies 18 (1): 42–53.
Bisegna, F., C. Burattini, M. Manganelli, L. Martirano, B. Mattoni, and L. Parise. 2016, June. “Adaptive Control for
Lighting, Shading and HVAC Systems in Near Zero Energy Buildings.” 2016 IEEE 16th International Conference on
Environment and Electrical Engineering (EEEIC), IEEE, 1–6.
Borisuit, A., F. Linhart, J. L. Scartezzini, and M. Münch. 2015. “Effects of Realistic Office Daylighting and Electric
Lighting Conditions on Visual Comfort, Alertness and Mood.” Lighting Research & Technology 47 (2): 192–209.
Canazei, M., M. Laner, S. Staggl, W. Pohl, P. Ragazzi, D. Magatti, E. Martinelli, and P. Di Trapani. 2016. “Room- and
Illumination-Related Effects of an Artificial Skylight.” Lighting Research & Technology 48 (5): 539–558.
Canazei, M., W. Pohl, H. R. Bliem, M. Martini, and E. M. Weiss. 2017. “Artificial Skylight Effects in a Windowless
Office Environment.” Building and Environment 124: 69–77.
Carter, D. J. 2002. “The Measured and Predicted Performance of Passive Solar Light Pipe Systems.” Lighting Research
& Technology 34 (1): 39–51.
Chaiwiwatworakul, P., and S. Chirarattananon. 2013. “Luminous Efficacies of Global and Diffuse Horizontal
Irradiances in a Tropical Region.” Renewable Energy 53: 148–158.
Chakrabarti, S., and S. Chakrabarti. 2002. “Rural Electrification Programme with Solar Energy in Remote Region – A
Case Study in an Island.” Energy Policy 30 (1): 33–42.
Chakraborty, S., P. Barua, S. Bhattacharjee, and S. Mazumdar. 2018. “Road Classification Based Energy Efficient
Design and its Validation for Indian Roads.” Light & Engineering 26 (2): 110–121.
CIE. 2003. Spatial Distribution of Daylight – CIE Standard General Sky. Standard CIE S 011/E:2003.
Colaco, S. G., C. P. Kurian, V. I. George, and A. M. Colaco. 2008. “Prospective Techniques of Effective Daylight
Harvesting in Commercial Buildings by Employing Window Glazing, Dynamic Shading Devices and Dimming
Control – A Literature Review.” Building Simulation 1 (1): 279–289.
Cuttle, C. 1983, June. “People and Windows in Workplaces.” Proceedings of the People and Physical Environment
Research Conference, Wellington, New Zealand, 203–212.
54 S. BHATTACHARYA ET AL.
http://orcid.org/0000-0002-3715-6901
http://orcid.org/0000-0003-1294-1986
Cuttle, C. 2010. “Towards the Third Stage of the Lighting Profession.” Lighting Research & Technology 42 (1): 73–93.
Cuttle, C. 2018. “A Fresh Approach to Interior Lighting Design: The Design Objective – Direct Flux Procedure.”
Lighting Research & Technology 50 (8): 1142–1163.
D’Agostino, D., and D. Parker. 2018. “A Framework for the Cost-Optimal Design of Nearly Zero Energy Buildings
(NZEBs) in Representative Climates Across Europe.” Energy 149: 814–829.
Dalla Costa, M. A., L. Schuch, L. Michels, C. Rech, J. R. Pinheiro, and G. H. Costa. 2010. “Autonomous Street Lighting
System Based on Solar Energy and LEDs.” 2010 IEEE International Conference on Industrial Technology, Via del
Mar, Chile. IEEE, 1143–1148.
Darula, S. 2018. “Review of the Current State and Future Development in Standardizing Natural Lighting in
Interiors.” Light & Engineering 26 (4): 5–26.
Deroisy, B., and A. Deneyer. 2017. “ANew Standard for Daylight: Towards a Daylight Revolution.” Proc. International
Conference on Lux Europa 2017, LES Slovenia: 340–343.
Dieste-Velasco, M. I., M. Díez-Mediavilla, D. Granados-López, D. González-Peña, and C. Alonso-Tristán. 2019.
“Performance of Global Luminous Efficacy Models and Proposal of a New Model for Daylighting in Burgos,
Spain.” Renewable Energy 133: 1000–1010.
Doulos, L. T., A. Kontadakis, E. N. Madias, M. Sinou, and A. Tsangrassoulis. 2019. “Minimizing Energy
Consumption for Artificial Lighting in a Typical Classroom of a Hellenic Public School Aiming for Near Zero
Energy Building Using LED DC Luminaires and Daylight Harvesting Systems.” Energy and Buildings 194: 201–
217.
Doulos, L. T., A. Tsangrassoulis, P. A. Kontaxis, A. Kontadakis, and F. V. Topalis. 2017. “Harvesting Daylight with
LED or T5 Fluorescent Lamps? The Role of Dimming.” Energy and Buildings 140: 336–347.
Doulos, L., A. Tsangrassoulis, and F. Topalis. 2008. “Quantifying Energy Savings in Daylight Responsive Systems:
The Role of Dimming Electronic Ballasts.” Energy and Buildings 40 (1): 36–50.
Duff, J., G. Antonutto, and S. Torres. 2016. “On the Calculation and Measurement of Mean Room Surface Exitance.”
Lighting Research & Technology 48 (3): 384–388.
Duff, J., K. Kelly, and C. Cuttle. 2017. “Spatial Brightness, Horizontal Illuminance and Mean Room Surface Exitance
in a Lighting Booth.” Lighting Research & Technology 49 (1): 5–15.
Duffy, J. F., and K. P. Wright. 2005. “Entrainment of the Human Circadian System by Light.” Journal of Biological
Rhythms 20 (4): 326–338.
Eltaweel, A., M. A. Mandour, Q. Lv, and Y.Su. 2020. “Daylight Distribution Improvement Using Automated
Prismatic Louvre.” Journal of Daylighting 7 (1): 84–92.
Fasi, M. A., and I. M. Budaiwi. 2015. “Energy Performance of Windows in Office Buildings Considering Daylight
Integration and Visual Comfort in hot Climates.” Energy and Buildings 108: 307–316.
Ferenčíková, M., and S. Darula. 2017. “Availability of Daylighting in School Operating Time.” Light & Engineering 25
(2): 71–78.
Galasiu, A. D., and J. A. Veitch. 2006. “Occupant Preferences and Satisfaction with the Luminous Environment and
Control Systems in Daylit Offices: A Literature Review.” Energy and Buildings 38 (7): 728–742.
Ge, A., P. Qiu, J. Cai, W. Wang, and J. Wang. 2014. “Hybrid Daylight/Light-Emitting Diode Illumination System for
Indoor Lighting.” Applied Optics 53 (9): 1869–1873.
Ghisi, E., and J. A. Tinker. 2005. “An Ideal Window Area Concept for Energy Efficient Integration of Daylight and
Artificial Light in Buildings.” Building and Environment 40 (1): 51–61.
Heerwagen, J. H., and D. R. Heerwagen. 1986. “Lighting and Psychological Comfort.” Lighting Design and
Application 16 (4): 47–51.
Henderson, S. T., and D. Hodgkiss. 1963. “The Spectral Energy Distribution of Daylight.” British Journal of Applied
Physics 14 (3): 125–131.
IES Calculation Procedures Committee. 1984. “Recommended Practice for the Calculation of Daylight Availability.”
Journal of the Illuminating Engineering Society 13 (4): 381–392.
Ihm, P., A. Nemri, and M. Krarti. 2009. “Estimation of Lighting Energy Savings from Daylighting.” Building and
Environment 44 (3): 509–514.
Joshi, M., R. L. Sawhney, and D. Buddhi. 2007. “Estimation of Luminous Efficacy of Daylight and Exterior
Illuminance for Composite Climate of Indore City in Mid Western India.” Renewable Energy 32 (8): 1363–1378.
Kabir, E., P. Kumar, S. Kumar, A. A. Adelodun, and K. H. Kim. 2018. “Solar Energy: Potential and Future Prospects.”
Renewable and Sustainable Energy Reviews 82: 894–900.
Kalogirou, S. A. 2004. “Environmental Benefits of Domestic Solar Energy Systems.” Energy Conversion and
Management 45 (18–19): 3075–3092.
Kalogirou, S. A. 2014. “Chapter 2-Environmental Characteristics.” Solar Energy Engineering, 92–95. doi:10.1016/
B978-0-12-397270-5.01001-3.
Kandilli, C., and K. Ulgen. 2008. “Solar Illumination and Estimating Daylight Availability of Global Solar Irradiance.”
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 30 (12): 1127–1140.
Karakoti, I., P. K. Das, and S. K. Singh. 2012. “Predicting Monthly Mean Daily Diffuse Radiation for India.” Applied
Energy 91 (1): 412–425.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 55
https://doi.org/10.1016/B978-0-12-397270-5.01001-3
https://doi.org/10.1016/B978-0-12-397270-5.01001-3
Kasten, F. 1993. “Discussion on the Relative Optical air Mass.” Lighting Research and Technology 25 (3): 129–130.
Kaufman, J. E. 1981. IES Lighting Handbook: 1981 Application Volume (Vol. 2). New York: Illuminating Engineering
Society of North America.
Kittler, R., R. Perez, and S. Darula. 1997. “A New Generation of Sky Standards.” Proceedings of the Lux Europa 97:
359–373.
Knoop, M., O. Stefani, B. Bueno, B. Matusiak, R. Hobday, A. Wirz-Justice, K. Martiny, et al. 2020. “Daylight: What
Makes the Difference?” Lighting Research & Technology 52 (3): 423–442.
Konstantoglou, M., and A. Tsangrassoulis. 2016. “Dynamic Operation of Daylighting and Shading Systems: A
Literature Review.” Renewable and Sustainable Energy Reviews 60: 268–283.
Kontadakis, A., A. Tsangrassoulis, L. Doulos, and S. Zerefos. 2018. “A Review of Light Shelf Designs for Daylit
Environments.” Sustainability 10 (1): 01–24.
Kose, B., and T. Kazanasmaz. 2020. “Applicability of a Prismatic Panel to Optimize Window Size and Depth of a
South-Facing Room for a Better Daylight Performance.” Light & Engineering 28 (4): 63–67.
Kousalyadevi, G., and G. Lavanya. 2019. “Optimal Investigation of Daylighting and Energy Efficiency in Industrial
Building Using Energy-Efficient Velux Daylighting Simulation.” Journal of Asian Architecture and Building
Engineering 18 (4): 271–284.
Lam, Y. M., and J. H. Xin. 2002. “Evaluation of the Quality of Different D65 Simulators for Visual Assessment.” Color
Research & Application 27 (4): 243–251.
Lee, H., K. Kim, J. Seo, and Y. Kim. 2017. “Effectiveness of a Perforated Light Shelf for Energy Saving.” Energy and
Buildings 144: 144–151.
Lim, Y. W., and C. Y. S. Heng. 2016. “Dynamic Internal Light Shelf for Tropical Daylighting in High-Rise Office
Buildings.” Building and Environment 106: 155–166.
Littlefair, P. J. 1985. “The Luminous Efficacy of Daylight: A Review.” Lighting Research & Technology 17 (4): 162–182.
Littlefair, P. J. 1995. “Light Shelves: Computer Assessment of Daylighting Performance.” Lighting Research and
Technology 27 (2): 79–91.
Mandal, P., and B. Roy. 2016, December. “Adaptive Dimming Scheme Based Daylight Pipe Integrated Indoor Lighting
System Under Perez All-Weather sky Model.” 2016 IEEE International WIE Conference on electrical and computer
Engineering (WIECON-ECE), IEEE: 221–224.
Maňková, L., J. Hraška, and M. Janak. 2009. “Simplified Determination of Indoor Daylight Illumination by Light
Pipes.” Slovak Journal of Civil Engineering 4: 22–30.
Markus, T. A. 1967. “The Function of Windows— A Reappraisal.” Building Science 2 (2): 97–121.
Mistrick, R., and A. Sarkar. 2005. “A Study of Daylight-Responsive Photosensor Control in Five Daylighted
Classrooms.” LEUKOS The Journal of the Illuminating Engineering Society of North America 01 (3): 51–74.
Molina, L. J., R. I. Maestre, and E. Lindawer. 2000. “A Model for Predicting the Angular Dependence of the Optical
Properties of Complex Windows Including Shading Devices.” Proceedings of PLEA, Cambridge, 765–770.
Muneer, T. 1996. “Evaluation of Luminous Efficacy Models Against Japanese Data: Technical Note.” International
Journal of Ambient Energy 17 (4): 212–216.
Muneer, T., and R. C. Angus. 1993. “Daylight Illuminance Models for the United Kingdom.” Lighting Research and
Technology 25 (3): 113–123.
Murdoch, J. B. 1994. Illumination Engineering: From Edison’s Lamp to the Laser. New York: Macmillan Publishing
Company.
Nilsson, A. M., and J. C. Jonsson. 2010. “Light-scattering Properties of a Venetian Blind Slat Used for Daylighting
Applications.” Solar Energy 84 (12): 2103–2111.
NREL. 2014. International Data. Accessed May 20, 2020. https://nsrdb.nrel.gov/about/international-data.html.
Okogbue, E. C., J. A. Adedokun, and B. Holmgren. 2008. “Hourly and Daily Global Photometric Illuminance and
Luminous Efficacy at a Tropical Station, Ile-Ife, Nigeria.” International Journal of Sustainable Energy 27 (3):
105–120.
Patil, K. N., S. N. Garg, and S. C. Kaushik. 2013. “Luminous Efficacy Model Validation and Computation of Solar
Illuminance for Different Climates of India.” Journal of Renewable and Sustainable Energy 5 (6): 063120.
Perez, R., P. Ineichen, R. Seals, J. Michalsky, and R. Stewart. 1990. “Modeling Daylight Availability and Irradiance
Components from Direct and Global Irradiance.” Solar Energy 44 (5): 271–289.
Pikas, E., M. Thalfeldt, and J. Kurnitski. 2014. “Cost Optimal and Nearly Zero Energy Building Solutions for Office
Buildings.” Energy and Buildings 74: 30–42.
Powell, I. 1996. “D65 Simulation with a Xenon Arc.” Applied Optics 35 (34): 6708–6713.
Protzman, J. B., and K. W. Houser. 2006. “LEDS for General Illumination: The State of the Science.” Leukos 3 (2):
121–142.
PVGIS. 2020. PVGIS-5 Geo-Temporal Irradiation Database. Accessed May 20, 2020. https://ec.europa.eu/jrc/en/
pvgis.
Rao, K. R., and T. N. Seshadri. 1961. “Solar Insolation Curves.” Indian Journal of Meteorology and Geophysics 12: 267–
272.
56 S. BHATTACHARYA ET AL.
https://nsrdb.nrel.gov/about/international-data.html
https://ec.europa.eu/jrc/en/pvgis
https://ec.europa.eu/jrc/en/pvgis
Raul, D., S. Pal, and B. Roy. 2015. “Application of Perez Daylight Efficacy Model for Kolkata.” Journal of The
Institution of Engineers (India): SeriesB 96 (4): 339–348.
Roenneberg, T., T. Kantermann, M. Juda, C. Vetter, and K. V. Allebrandt. 2013. “Light and the Human Circadian
Clock.” In Circadian Clocks. Handbook of Experimental Pharmacology. Vol. 217, edited by A. Kramer and M.
Merrow, 311–331. Berlin: Springer. doi:10.1007/978-3-642-25950-0_13.
Roy, B., and S. R. Bandyopadhya. 2002. “Interreflection Calculation Under Daylight by Monte Carlo Method.”
Journal of Optics 31 (3): 95–104.
Samuhatananon, S., S. Chirarattananon, and P. Chirarattananon. 2011. “An Experimental and Analytical Study of
Transmission of Daylight Through Circular Light Pipes.” Leukos 7 (4): 203–219.
Sengupta, M., Y. Xie, A. Lopez, A. Habte, G. Maclaurin, and J. Shelby. 2018. “The National Solar Radiation Data Base
(NSRDB).” Renewable and Sustainable Energy Reviews 89: 51–60.
Shahsavari, A., and M. Akbari. 2018. “Potential of Solar Energy in Developing Countries for Reducing Energy-
Related Emissions.” Renewable and Sustainable Energy Reviews 90: 275–291.
Shin, J. Y., G. Y. Yun, and J. T. Kim. 2012. “Evaluation of Daylighting Effectiveness and Energy Saving Potentials of
Light-Pipe Systems in Buildings.” Indoor and Built Environment 21 (1): 129–136.
Shishegar, N., and M. Boubekri. 2016. “April. Natural Light and Productivity: Analyzing the Impacts of Daylighting on
Students’ and Workers’ Health and Alertness.” Proceedings of the International Conference on “health, Biological
and life science” (HBLS-16), Istanbul, Turkey, 18–19.
Soler, A., and P. Oteiza. 1997. “Light Shelf Performance in Madrid, Spain.” Building and Environment 32 (2): 87–93.
Solovyov, A. K. 2017. “Reflective Facades and Their Influence on Illumination of Nearby Buildings.” Light &
Engineering 25 (2): 88–93.
Thayer, B. M. 1995. “Daylighting and Productivity at Lockheed.” Solar Today 9 (3): 26–29.
Tsangrassoulis, A., L. Doulos, A. Kontadakis, and A. Drakou. 2019. “A Methodology to Model the Performance of a
Dynamic Mirror Light-Shelf Based on Solar Radiant Flux Impinging on the Window.” Proceedings of the 16th
IBPSA Conference, International Building Performance Simulation Association, Rome, Italy, 1028–1035.
doi:10.26868/25222708.2019.210483.
Turan, I., A. Chegut, D. Fink, and C. Reinhart. 2020. “The Value of Daylight in Office Spaces.” Building and
Environment 168: 106503.
Van Derlofske, J. F., and T. A. Hough. 2004. “Analytical Model of Flux Propagation in Light-Pipe Systems.” Optical
Engineering 43 (7): 1503–1511.
Wang, J., and Y. Yang. 2016. “Energy, Exergy and Environmental Analysis of a Hybrid Combined Cooling Heating
and Power System Utilizing Biomass and Solar Energy.” Energy Conversion and Management 124: 566–577.
Woelders, T., D. G. Beersma, M. C. Gordijn, R. A. Hut, and E. J. Wams. 2017. “Daily Light Exposure Patterns Reveal
Phase and Period of the Human Circadian Clock.” Journal of Biological Rhythms 32 (3): 274–286.
Wright, J., R. Perez, and J. J. Michalsky. 1989. “Luminous Efficacy of Direct Irradiance: Variations with Insolation and
Moisture Conditions.” Solar Energy 42 (5): 387–394.
Xue, P., C. M. Mak, and H. D. Cheung. 2014. “The Effects of Daylighting and Human Behavior on Luminous
Comfort in Residential Buildings: A Questionnaire Survey.” Building and Environment 81: 51–59.
Yao, J. 2018. “Daylighting Performance of Manual Solar Shades.” Light & Engineering 26 (1): 99–104.
Yasukouchi, A., T. Maeda, K. Hara, and H. Furuune. 2019. “Non-visual Effects of Diurnal Exposure to an Artificial
Skylight, Including Nocturnal Melatonin Suppression.” Journal of Physiological Anthropology 38 (1): 1–12.
Yun, G. Y., T. Hwang, and J. T. Kim. 2010. “Performance Prediction byModelling of a Light-Pipe SystemUsed Under
the Climate Conditions of Korea.” Indoor and Built Environment 19 (1): 137–144.
Zazzini, P., A. Romano, A. Di Lorenzo, V. Portaluri, and A. Di Crescenzo. 2020. “Experimental Analysis of the
Performance of Light Shelves in Different Geometrical Configurations Through the Scale Model Approach.”
Journal of Daylighting 7 (1): 37–56.
INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 57
https://doi.org/10.1007/978-3-642-25950-0_13
https://doi.org/10.26868/25222708.2019.210483
	Abstract
	1. Introduction
	2. Methodology
	2.1. Geographical description and collection of solar irradiation data
	2.2. The Perez model
	2.3. The IESNA recommended calculation procedure of estimating daylight availability
	2.4. Measurement of solar illuminance
	2.5. Estimation of indoor daylight illuminance with window shading control
	2.6. Estimation of indoor daylight illuminance with light pipes
	2.7. Estimation of indoor daylight illuminance with skylights
	2.8. Estimation of indoor daylight illuminance with light shelves
	3. Results
	3.1. Estimations from the Perez model
	3.1.1. Average global and diffuse luminous efficacy
	3.1.2. Average global and diffuse horizontal illuminance
	3.2. Estimations from the IESNA recommended calculation procedure
	3.2.1. Direct horizontal solar illuminance and horizontal sky illuminance
	3.2.2. Total daylight illuminance
	3.3. Measured solar illuminance and comparison with computed illuminance levels
	3.4. Estimations from window shading control simulation
	3.5. Estimations from light pipe simulation
	3.6. Estimations from skylight simulation
	3.7. Estimations from light shelf simulation
	4. Discussion
	4.1. Percentage cumulative frequency distribution of solar illuminance and luminous efficacy parameters
	4.2. Correlation between solar irradiation and solar illuminance
	4.3. Statistical analysis of estimated indoor illuminance levels
	5. Conclusion
	Acknowledgements
	Disclosure statement
	ORCID
	References
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