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978-1-5386-1887-5/17/$31.00 ©2017 IEEE
Building performance study of Indira Awas Yojana
for smart village
Ronita Bardhan
Centre for Urban Science and Engineering
Indian Institute of Technology Bombay
Mumbai- 400076, India
ronita.bardhan@iitb.ac.in
Ramit Debnath
Centre for Urban Science and Engineering
Indian Institute of Technology Bombay
Mumbai- 400076, India
ramit.debnath@outlook.com
Abstract— In this study, we evaluated building performance
of rural houses sanctioned under the Indira Awas Yojana (IAY)
of Government of India. The performance analysis was
conducted in two phases: i) climate-based dynamic building
energy simulations were performed for indoor thermal analysis
for the typical Indian Summer months; ii) indoor air quality was
subjectively investigated using the metric of local mean age of air
(LMA). Three climatic zones were considered for investigation,
namely, composite, hot-dry and hot-humid climate. Results
showed that the average operative temperature of the living room
for Madhya Pradesh, Rajasthan and Tamil Nadu varied between
42.12 – 22.91°C; 45.41 – 26.46°C and 39.72 – 27.22°C,
respectively. The LMA values of the houses under investigation
were evaluated to be about 1.30, 0.02 and 30.00 minutes,
respectively, indicating that the Rajasthan design performed
better in IAQ assessment. However, the Madhya Pradesh design
performed better in the thermal comfort assessment. The large
inter-variability of indoor thermal profiles and LMA values
indicated the need for standardized habitat design guidelines for
rural areas in India.
Keywords—building simulations; rural buildings; housing
policies; low-income community; data-driven approach
I. I
NTRODUCTION
The Ministry of Rural Development (MoRD), Government
of India (GoI) revealed that there is an urgent need for 48.81
million housing units, of which 43.93 million households (or
90%) belongs to below-poverty line [1]. Indira Awas Yojana
(IAY) is one such social welfare program, which intends to fill
the housing crisis among the rural communities. It was
launched in 1985-86, with the latest guidelines issued in 2012,
with the aim of providing better quality housing to the rural
poor. The IAY was revised and relaunched as Pradhan Mantri
Awas Yojana (PMAY) in June, 2015 [2].
It is expected that about 61.89 million houses for
economically weaker section will be constructed by 2017 to
fulfill the declaration of “Housing for All - 2022” by the GoI
under the National Housing and Habitat Policy (1998), which
also include the targets for IAY [1]. With the requirement of
such large built-up areas, and construction materials, it
become a necessity to evaluate the building performance in
purview of the design and building materials. Effective
housing design being a critical factor of good quality of life
[3], [4], it becomes pertinent to investigate its performance at
the planning phase of such large housing stock. This will
provide an empirical background for rational decision making
in design thinking and development of the future building
stocks under IAY [5]–[7]. Thus, promoting sustainability at
the bottom of the pyramid for rural community.
The rural population in India depends greatly on traditional
non-cleaner fuels for cooking, which corresponds to high
household air pollution (HAP) [8]. Moreover, lack of proper
ventilation strategies in these houses, exaggerate this situation,
leading to increased stress on the National Burden of Diseases
[9], [10]. In this study, we evaluate the indoor air quality
(IAQ) by considering a subjective IAQ indicator, known as
the local mean age of air (LMA). LMA is defined as the
average time for fresh air to transport from an inlet to any part
of the room or space [11]. Thus, we assume that higher the
value of LMA at a given instance, higher is the air stagnation
at a given instance leader to poorer IAQ. LMA is a subjective
measure of IAQ and it represents the state of ‘freshness’ of the
indoor spaces [11].
The lack of proper ventilation in these houses also causes
prolonged discomfort to the occupants through higher indoor
mean radiant and operative temperature, which coerce the
occupants to resort to energy intensive devices for cooling,
like additional table fan, coolers or even air conditioners [12].
Higher indoor thermal temperature is also modulated by the
choice of building materials and the space usage in the
buildings. Application of appropriate and cost-effective
building materials and ventilation strategies can drastically
improve the IAQ and the thermal profile in these low-income
dwellings [12], [13].
In this study, we evaluated thermal comfort and IAQ status
of typical IAY houses across three climatic zones of India, and
compared them with the ASHRAE-55 standards for thermal
comfort [14], [15]. Three states were chosen namely, Madhya
Pradesh, Rajasthan and Tamil Nadu to represent composite,
hot-dry and hot-humid climate, respectively. The results were
compared and inferences were drawn towards a data-driven
pathway for sustainable housing design for rural areas under
the IAY. The larger goal of this study is to contribute towards
the development of sustainable rural housing policies, which
still remains a large blind spot in the Indian housing policy
and research [8].
Kitchen
Living Room
Toilet Wash-area
Cookstove
Kitchen
Anti-Room Living Room
Cookstove
Living
room wit h
kitchen
Toilet Cookstove
ABC
Fig. 1 Typical layout of IAY house in (A) Madhya Pradesh; (B) Rajasthan; (C) Tamil Nadu.
II. D
ATA AND
M
ETHODS
A. Modelling of the IAY houses
The housing models were adopted from the official
specifications of the IAY, GoI (see Fig 1) [16]. These designs
were proposed as per climatic, economic and socio-cultural
dynamics of the place by the government. The input
parameters of the models are illustrated in Table 1.
Table 1. Modelling input parameters
Model # A B C
Climatic zones Hot-Humid Hot-Dry Composite
Location (States)
Madhya
Pradesh Rajasthan Tamil Nadu
Dimensions
Built-up Area (m
2
) 25.80 20.70 20.26
Opening to wall
ratio (OWR) 11.76% 18.65% 11.20%
Height (in m) 2.8 2.3 3
Kitchen OWR 7.32% 14.63% 11.21%
Kitchen Volume
(in m
3
) 14.58 6.78 44.01
Source: [16]
The models were assumed to have a solid fuel cookstove,
modelled as a heat source of 15,000 W/m
2
, which is
equivalent to the energy density of 5 kg of firewood (the
average amount required per household for cooking) [11],
[17]. These assumptions were made owing to the demography
that the IAY housing caters too. The walls were assumed to be
constructed of burned bricks, with a U-value of 2.04 W/K.m
2
,
with a wall thickness of 250mm, RCC-roof of 150 mm
thickness (U value - 2.5 W/K.m
2
) and single-glazing clear
glass of 3mm thickness, having a U-value of 6.21 W/K.m
2
[18]. The operating schedule of the windows was assumed to
be open for 100% of the time, whereas for the doors, it was
assumed to be open for 10% of the time [11]. Air infiltration
through cracks and gaps was also considered during the
modelling stage [11].
B. Simulations
EnergyPlus v8.3 was used to perform the climate-based
dynamic simulations of the study buildings (see Fig 1). The
hourly weather-data was imported from the weather stations of
the following places: Bhopal (Madhya Pradesh), Coimbatore
(Tamil Nadu) and Jaisalmer (Rajasthan). This formed the first
stage of the building performance analysis. The next stage was
the multi-zonal modelling of the airflow network for LMA
calculations.
The rural houses are predominantly naturally ventilated,
which has inherent uncertainties associated with them [11].
This was addressed by assuming a deterministic approach in
the airflow calculations using 3D steady-state Reynolds-
averaged Navier Strokes (RANS) equations with standard k-ɛ
turbulence model [8], [11]. Wind-driven natural ventilation
was assumed to be the primary form of cooling and IAQ
regulator in the study houses (see Fig 1), such that the air is
assumed to enter from one opening and exit through the other
in a unidirectional manner.
The set of RANS equations with the standard k-ɛ
turbulence model was solved using the CFD solver of
DesignBuilder v4.7. Hexahedral computational grids were
chosen for the CFD analysis. This choice was driven by their
accurate result delivery for rectangular geometries [19]. Grid
interdependency tests were conducted, as per the best practice
CFD guidelines and the medium mesh was selected due to its
low computing resource requirement. The solutions were
converged using the criteria of 0.01% of RMS residuals for
mass and momentum equations in 5,000 iterations. The
second-order ‘UPWIND’ discretization scheme was used for
numerical convergence of the solutions. The CFD verification
was carried out using the airflow simulations studies of
Debnath, Bardhan and Banerjee [11].
C. Mathematical background
a. Local Mean Age of Air
The local mean age of air, τ, was obtained from the
following transport equation:
=
+
+1,
where ν and ν
t
are the laminar and turbulent kinematic
viscosities, respectively, σ is the laminar Schmidt number of
air and σ
t
is the turbulent Schmidt number for ages of air. The
detailed mathematical background of LMA calculation using
steady-state RANS equations can be referred from [11], [20].
b. Thermal comfort calculations
Thermal comfort is defined as that “condition of mind
which expresses satisfaction with the thermal environment"
[14]. The thermal temperature that our body experience in an
indoor space is known as operative temperature (t
o
). It is a
combined effect of mean radiant temperature (t
r
) and the air
temperature (t
db
). Mean radiant temperature (t
r
), is defined as
‘the uniform surface temperature of an imaginary black
enclosure in which an occupant would exchange the same
amount of radiant heat as in the actual non-uniform space’
[21]. The t
o
and t
r
are given as follows:
=(
+
)
2
=
→
4
=1
+
ԑ
,
→
,
=1
+
,
4
III. R
ESULTS AND
D
ISCUSSIONS
In this study, building performance analysis of IAY houses
were conducted in two steps: i) climate-based thermal
performance simulations of the study buildings (see Fig 1)
were performed; ii) IAQ analysis using the surrogate of LMA
was performed using steady-state RANS equations with
standard k-ɛ turbulence model. Results show that the average
operative temperature (t
0
) of the living room for Madhya
Pradesh, Rajasthan and Tamil Nadu varied between a range of
42.12– 22.91°C; 45.41 – 26.46°C and 39.72 – 27.22°C,
respectively.
a. Thermal performance analysis
Results show that the average operative temperature (t
0
) of
the living room for Madhya Pradesh, Rajasthan and Tamil
Nadu varied between a range of 42.12– 22.91°C; 45.41 –
26.46°C and 39.72 – 27.22°C, respectively. Whereas the
outside temperature range was between 43.51 – 15.60 °C for
Madhya Pradesh; 45.60-19.80°C for Rajasthan, and 41.80-
21.70 °C for Tamil Nadu. Thermal comfort simulations for the
summer months showed that all the houses were above the
ASHRAE-55 desired indoor temperature of 26
°
C [21]. Fig. 2
and Fig. 3 illustrates the variation of operative temperatures in
the living room and the kitchen of the buildings under
investigation (see Fig 1).
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April May June July August September
Temperature in ºC
Madhya Pradesh Rajasthan
Tamil Nadu ASHRAE 55 : Desirable Temp
April May June July Aug Sep
Fig. 2 Operative temperature in the living room.
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April May June July August September
Temperature in ºC
Madhya Pradesh Rajasthan Tamil Nadu
April May June July Aug Sep
Fig. 3 Operative temperature in the kitchen.
The high values of the operative temperature in the study
buildings in the respective climatic zones indicate the non-
compliance of building design with the ASHRAE-55
standards. This have greater implications in terms of extended
discomfort hours and forcing the occupants to resort to
energy-extensive devices for cooling and ventilation. This will
impose added economic burden on the rural households, who
are already under resource stress. The combined thermal
performance plot of the IAY housed in the three climatic
zones is illustrated in Fig 4. The high operative temperature in
these houses will have cumulative adverse effect on the health
of the occupants, which inadvertently effects their Disability
Adjusted Life Years (DALYs) [22].
(1)
(2)
(3)
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April May June July August S eptember
Temperature in ºC
OT: Living room with Kitchen Outside Temperature
ASHRAE 55 : Desirable Temp
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Temperature in ºC
OT: Anti-room OT: Living Roo m OT: Kitchen
Outside Temperature ASHRAE 55 : Desirable Tem p
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April May June July Augu st September
Temperature in ºC
OT-Living room OT-Kitchen
Outside Temperature ASHRAE 55 : Desirable Temp
A. Madhya Pradesh B. Rajasthan C. Tamil Nadu
Fig. 4 Operative temperature in the three IAY houses.
b. Indoor air quality analysis: LMA indicator
The local mean age of air (LMA) analysis shows that the
houses with an external kitchen (A and B) are fresher than C
(see Fig 5). This is evident from the Fig 5, where the color
bands correspond to different values of age of air (in seconds).
In Fig. 5A, i.e. the IAY design for Madhya Pradesh, showed
an average age of air of 3.5 minutes (208.24 sec) in the living
room, whereas the kitchen with an OWR of about 8% had the
age of air close to 1.3 minutes (78.09 sec). Similarly, for the
IAY design for Rajasthan, the age of air for the living room
was 11.28 minutes (677.36 sec), and for the kitchen, it was
around 0.025 minutes (1.5 sec).
The IAY design for Tamil Nadu, that included a kitchen
and multi-purpose living room in the same floor space without
any inner partition or wall (see Fig. 1C), showed a LMA range
of around 25 - 30 minutes, at an OWR of 11% (refer Fig. 5C).
Subjectively, it indicates that when the fresh draft of air is
entering the living space, the age of accumulated air is 30 min.
This means higher accumulation of HAP for an extended
period, which could implicate very adversely on the health of
the occupants. Higher LMA would mean extended exposure of
HAP to the women and children, which cumulatively add-on
to the National Burden of Diseases [9].
It becomes evident that the current IAY designs, do not
necessarily comply to the sustainability standards. However,
the issues of high indoor temperature and HAP levels can be
resolves through effective low-cost design strategies using
appropriate building materials, enabling cross-ventilation in
the living spaces and kitchens, and through sensitization about
the benefits of sustainable design. However, for the later part a
larger policy pathway would be required which could integrate
local practices and socio-economic layers into the design
phase. Hence, we present a sustainable habitat design
framework (see Fig. 6) for affordable houses under the IAY
program, such that these houses promote healthy living and a
better quality of life.
0.00
26.03
52.06
78.09
104.12
130.15
156.18
182.21
208.24
234.27
260.30
286.33
Age of air (sec )
A
0.00
84.57
169.34
254.01
338.68
423.35
508.02
592.69
677.36
762.03
846.70
931.37
Age of air (sec)
B
0.00
155.47
310.94
466.41
621.88
777.35
932.83
1088.30
1243.77
1399.24
1554.71
1710.18
Age of air (sec)
C
Fig. 5 LMA values on xy plane at a working height of 0.5
metres [(A) Madhya Pradesh; (B) Rajasthan; (C) Tamil Nadu].
Engineered comfort
Perceived comfort
Human Health
Indoor Air
Quality
Indoor Thermal
Comfort
Human Development
Rural
Housing
design
Affordability
Smart Village through habitat design
Fig. 6 Framework for smart village through effective habitat
design
This framework has two core design thinking process: i)
promotion of comfortable indoor operative temperature; and
ii) enabling better cross-ventilation for enhanced indoor air
quality. These processes will be balanced with the
affordability goals of the houses, such that it fulfils the core
goals of ‘Housing for All-2022’ scheme, and contribute
towards overall human development.
IV. C
ONCLUSION
In this study, building performance analysis of affordable
housing under the IAY scheme of the Government of India
was conducted for three different climatic zones of India. The
performance criteria were divided in two stages, first was the
assessment of thermal performance of the houses, and second
stage was the IAQ assessment through a subjective indicator
of indoor freshness (LMA). It was observed that houses with
external kitchen had better IAQ, compared to the indoor
kitchen. However, the indoor thermal profile remained high in
all the study buildings, indicating the need for suitable
ventilation strategies with appropriate building materials that
can compensate higher indoor temperatures. These findings
were cumulatively presented in the form of a data-driven
sustainable habitat design framework for rural areas, such that
the future housing stock in IAY will inherently provide better
comfort conditions with effective ventilation for better IAQ.
This framework also enables in determining the inherent
relationships between built parameters and IAQ in such
affordable habitats. This study is limited to the definition of a
smart village, where the housing is addressed as the centrum
of sustainable development. Future work is needed to
formalize this framework with experimental evidences, for
adopting it as an affordable housing policy mechanism for
rural areas in India.
A
CKNOWLEDGEMENT
Part of this work is supported by the MHRD-GoI Grant
No. 14MHRD005, and IRCC- IIT Bombay Grant No.
16IRCCSG1015. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the
authors and do not necessarily reflect the views of the MHRD-
GoI, and/or IRCC-IITB.
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B
IOGRAPHIES
RONITA
BARDHAN
holds a Ph.D. degree in
Urban Engineering
from the University of
Tokyo, as a MEXT
scholar. She is Assistant
Professor at the Centre
for Urban Science and
Engineering, Indian
Institute of Technology
Bombay and Visiting
Assistant Professor at the Civil and Environmental Engineering
Department, Stanford University, USA. She is a member of IEEE-
GRSS, the Eastern Asia Society for Transportation Studies, the
Council of Architecture, Govt. of India (GoI) and the Institute of
Town Planner, India. She is a recipient of Building Energy Efficiency
Higher & Advanced Network (BHAVAN) Felowship-2016 supported
by the Department of Science and Technology, GoI, and the Indo-
U.S. Science and Technology Forum (IUSSTF). Her research
interests include data-driven design heuristics for sustainable habitat
design, building energy simulations and modelling at an urban scale,
climate resiliency study of urban forms and pro-environmental
behavior.
R
AMIT
D
EBNATH
is a
Research Associate at the Centre
for Urban Science and
Engineering, Indian Institute of
Technology Bombay. He also
worked as a Visiting Student
Researcher at the Civil and
Environmental Engineering
Department, Stanford
University, USA. He holds a
M.Tech. in Technology and
Development from the Indian
Institute of Technology Bombay and has a B. Tech (honors) in
Electrical & Electronics Engineering. His research interests include
investigation of building and energy interactions in low-income
communities in urban areas of India, engineering for development
and data-driven design heuristics for low-income habitats.
