COMBINED EFFECT OF ENERGY EFFICIENCY MEASURES
AND THERMAL ADAPTATION ON AIR CONDITIONED
BUILDING IN WARM CLIMATIC CONDITIONS OF INDIA
by
Shivraj Dhaka, Jyotirmay Mathur, Vishal Garg
Report No: IIIT/TR/2012/-1
Centre for IT in Building ScienceInternational Institute of Information Technology
Hyderabad - 500 032, INDIASeptember 2012
1
COMBINED EFFECT OF ENERGY EFFICIENCY MEASURES AND
THERMAL ADAPTATION ON AIR CONDITIONED BUILDING IN WARM
CLIMATIC CONDITIONS OF INDIA
Shivraj Dhaka1, Jyotirmay Mathur
1*, Vishal Garg
2
1Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur,
India 2Centre for IT in Building Science, International Institute of Information Technology,
Hyderabad, India
Abstract
This study evaluates improvement in energy efficiency of an air conditioned building block
employing energy conservation measures (ECMs) recommended by Indian Energy
Conservation Building Code -2007 (ECBC) through prescriptive route. First part, evaluates
energy savings by implementing five ECMs of envelope independently and two combinations
of ECMs keeping constant thermostat setting throughout the year. In second part of the study
same ECMs are considered to the subject building model allowing thermostat settings as per
thermal adaptation resulting from change in outdoor temperature. Actual measurements were
taken and simulation model was finetuned. Annual energy consumption of building is used to
evaluate the effect of individual ECMs and their combinations on both part of the study, i.e.
fixed thermostat and adaptive thermostat settings. The simulation result shows that together
with combination of all ECMs recommended by ECBC, small buildings can save up to 40%
energy consumption as compared to buildings built with conventionally practiced
specifications of India. Effect of thermal adaptation itself offers up to 16% energy saving
opportunity in small buildings considering adaptive thermostat settings. The potential of
energy conservation through ECMs suggested by ECBC and adaptive set point gets
significantly reduced for large size buildings having high internal heat gains.
Key words: Building Code; Energy Efficiency Measure; Energy Efficiency; Thermal
Adaptation
Nomenclature
ECBC Energy Conservation Building Code LPD Lighting Power Density (W/m2)
ECM Energy Conservation Measure Tmmo, To Mean monthly outdoor dry bulb temperate (oC)
EPD Equipment Power Density (W/m2) Tn Neutral/ comfort temperature, (
oC)
1. Introduction
An efficient building envelope with appropriate design consideration can reduce energy
consumption and downsize the heating ventilation and air conditioning (HVAC) system. It is
the interface between indoor and outdoor conditions. In warm climatic conditions, prevention
of heat gain through envelope is the best way to conserve energy, therefore building envelope
should be climate responsive.
*Corresponding author: Dr.-Ing Jyotirmay Mathur, Head-Centre for Energy and Environment MNIT Jaipur,
Tel: +91-141-2713211; E-mail: [email protected]
2
Energy consumption in building sector is continuously increasing all around the world;
Synnefa et al. concluded that nearly 60% of the net electricity consumption in the OECD
(Organisation for Economic Co-operation and Development) economies is from the building
sector [1]. This sector represents about 33% of electricity consumption in India, with
commercial sector and residential sector accounting for 8% and 25% respectively. It is
estimated that ECBC compliant buildings may consume about 40% less energy than
conventional practiced buildings in India and nationwide enforcement of the building code
could result in annual saving of 1.7 billion kWh units [2]. In the residential sector, building
size and its location are the key factors for energy consumption as small buildings or flats
need less energy as there is less conditioned and transfer area, and also less occupancy. The
amount and type of energy used in building is mainly due to variation related to weather,
architectural design, and envelope features such as wall, roof, and glazing. These factors
affect energy consumption of buildings a lot. Chirarattananona et al.conducted study at
tropical climate of Thailand revealed that insulation of wall decrease the cooling coil load
from 83.0 to 44.1 kWh/m2/yr [3] whereas similar type of study carried out at hot & humid
climate of Dubai (UAE) demonstrated 30% energy saving by wall insulation [4].
Reflectivity of roof has become important factor in warm climatic conditions being easy and
inexpensive measure to conserve energy as well as to improve thermal comfort conations.
Synnefa et al. demonstrated that increasing roof reflectivity from 0.3 to 0.5 decreases energy
consumption by 15% to 30% in hot climate [1] whereas Bhatia et al. conducted study at a
multi storey learning center of Hyderabad to examine the effect of reflective roof on cooling
energy as well as building energy consumption in composite climate of India. It revealed that
white coating reduce total building energy consumption by 5% [5]. Cooling and heating
requirement caused by residential roof accounts for about 4% of the whole building envelope
and 20% of the top floor in hot summer and cold winter [6-7]. Energy efficient glazing can
reduce the energy consumption and CO2 emissions by 25% and 7.1% respectively [8]. Roof
is responsible for dominant heat gain and it is predicted that insulation over roof provide
maximum energy savings compare to other envelope measures whereas South oriented wall
gives least energy savings in warm climate [9-11]. Thus, building envelope affects heat gain
and also plays important role in selection of air conditioning system. ASHRAE 90.1-2007
suggested climate based envelope specification to improve energy efficiency of buildings
although it is not considered in this study [12]. This study is aimed to use envelope
specification of ECBC to evaluate energy efficiency in different warm climatic conditions of
the country.
Many researchers such as Humphreys, de Dear, Nicol, Brager, etc. conducted field studies
and concluded that occupants feel thermally comfortable at high elevated temperature which
is beyond the thermal comfort conditions defined by ASHRAE 55-2004 [13]. This is due to
physiological, psychological, and behavioural adaptation of occupants. Approach of adaptive
thermal comfort also offers energy conservation in buildings. Field study carried out at
naturally ventilated building concluded that occupants perceive thermally comfortable up to
30oC without much ventilation [14]. Mui and Chan demonstrated that with the integration of
adaptive comfort temperature (ACT) model about 7% energy could be saved in office
buildings [15]. Similar type of study carried out at Thailand demonstrated that every increase
in set point by 1oC (from 22 to 28
oC) gives a mean energy saving of about 6.14% [16].
3
Pioneers researchers Auliciems and de Dear carried out field research and proposed comfort
temperature equation, Tn = 0.31To + 17.6 for conditioned and non-air-conditioned buildings
[17]. Above equation is used in this study to work out neutral temperature for three warm
climatic conditions of India. Then, this monthly varying neutral temperature is used as
thermostat of the air conditioner. Based on review, it is clear that insulation of roof gives
maximum energy saving in warm climates and use of adaptive concept with this measure
would result in significant energy conservation.
The purpose of study is to quantify energy saving potential considering envelope measures of
ECBC initially keeping fixed set point, and then by varying it as per thermal adaptation
approach. The effect of thermal adaptation is evaluated in three representative cities located
in hot & dry, warm & humid, and composite climatic zones of India.
2. Methodology
2.1. Site and building block
The study has been conducted at institute‟s hostel building at Hyderabad (17.45o, 78.47
o, and
545m above sea level). The city has high temperature during summer, cold winter, and low
humidity in summer but high during rain, and high solar radiation in all the seasons except
rainy season. The summer mid day high and winter night low temperature is about 45oC and
4oC respectively. Hot as well as cold wind blows during summer and winter time, cold strong
wind during rain and hazy sky occasionally. The mean monthly outdoor dry bulb temperature
varies from 20 to 35oC. City has been considered under composite climate of India.
Top floor hostel room of wing „D‟ of old boy‟s hostel (OBH) has been chosen for this study.
Photograph A and B of Figure 1 shows the geographic location and elevation of analyzed
hostel building. The investigated part of the building was six year old and it was built with
concrete roof and double brick wall with beam type heavy weight construction. Building was
constructed in cross shaped (107x107m) structure to avail the effect of across ventilation to
all wings of the building. Every room has one door facing to the corridor and windows on
both sides to provide cross ventilation. Transverse iron jail (X shaped) was put on corridor
wall. Hostel had room size of 3.6x2.4m (room area 8.64m2), floor to ceiling height of 3.2m
(room volume 27.7m2), window openings of 1.34×0.65m, window shade of 0.91x0.6m,
opaque door of 1.98x1.0m, and a corridor of 1.35m wide to front side of the hostel rooms
which was used as walkway to the neighbouring rooms. Windows were quite ordinary and
had single clear glass of 0.006m thickness; each window had two glass panes and four
thermal breaks. Window glass panes were operable to outside in case of rear window and
inside in case of corridor window. Iron frames were used for the construction of windows as
well as door. The U-value of glass was 5.8W/m2-oC, and solar heat coefficient and direct
solar transmission were 0.81 and 0.8 respectively. Table 1 illustrates the construction details
of existing building block. Construction of hostel building was similar to conventional
construction practices of India. All the rooms had single occupancy and equipped with single
fan, a computer, and a fluorescent tube light. Internal load was not much affecting energy
consumption being less compare to ECBC compliant buildings.
2.2. Temperature measurements Three parameters were recorded from the hostel room as roof inside and outside surface
temperature, and room air temperature. Minco S667 PT100/3 RTD sensors (time constant 1.3
second) were installed at the centre of roof inside as well as outside to record surface
4
temperature. Campbell Scientific 108-L probe was used to record room air temperature.
Photograph D and E of Figure 1 represents positioning and location of roof surface
temperature and room air temperature probe respectively. The accuracy of the probe was
±0.2°C over 0° to 70°C temperature and time constant was 30 to 60 second at wind speed of
5m/s. This probe was suspended 1m below to the inside roof and about 0.75m away from
wall. Image C of Figure 1 shows data logger -„Campbell Scientific CR1000‟ and connections.
It was used to record temperature measurements at the interval of 30 second start from June
26, 23:00pm to July 31, 23:00pm. Later on, measurements had been averaged out on hourly
basis to compare with the simulation outputs.
Table 1 Construction details of the building block
Material
(Outer to inner layer)
Roof
Thickness (m)
Wall
thickness (m)
Floor
thickness (m)
Gypsum Plastering 0.0127 0.0127 0.0127
Sand and Gravel 0.0254 - 0.0254
Concrete slab medium density 0.1016 - 0.1016
Brick - 0.2032 -
Gypsum Plastering 0.0127 0.0127 0.0127
Cork tiles - - 0.06
Assembly U-value (W/m2/oC) 3.8 1.9 3.1
2.3. Simulation model
Simulation model of investigated part of the building was modeled in DesignBuilder (version
2.100.25) by specifying all the information of actual building block such as azimuth angle,
envelope (wall, roof and glazing) properties, occupants schedule, lighting schedule, fan
schedule, shading devices etc. Photograph F and G of Figure 1 shows the plan and
axonometric view of the building block. Simulation was carried out using EnergyPlus
(version V4.0.0.024) building simulation program. Layer by layer construction (outside to
inside) of wall as well as roof has been given in Fig.2.
Actual measurements obtained from the building block were compared to simulation outputs.
In order to find good congruence between measurements and corresponding simulation
outputs, a series of alterations were carried to the simulation model. Solar absorptance was
varied from 0.1 to 0.25 in step of 0.05, thickness of sand and gravel (layer) was modified
from 0.00635m to 0.0508m in step of 0.00635m and size of brick was altered from 0.23m to
0.25m in step of 0.00635m. Mean bias error (MBE) and coefficient of variation root mean
square error Cv(RMSE) was calculated using equation 2 to 5, during alterations to the
building model. These errors below 10% and 15 are considered as good congruence between
measured and simulated parameters [5].
(equation 2)
Where: M is the measured value during the time interval, S is the simulated during the same
time interval. Root mean square error was calculated using equation 3.
(equation 3)
Here, N is the number of time intervals (720 hours) during monitoring period. The mean of
5
the measured data for the period is defined in equation 4.
(equation 4)
Following equation 5 was used to compute coefficient of variation root mean square error.
Once the simulation model shows errors within permissible limits, this ensures further use of
simulation model.
(equation 5)
2.4. Final simulation model
Later on, simulation model was changed from naturally ventilated building block into air
conditioned building block model by specifying HVAC related inputs, infiltration, fan
schedule etc. Simulation model was having a packaged type air conditioner unit of COP 3.1
(average performance), a value recommended by ECBC. This simulation model was used as
the basic subject building model to examine the effect of envelope measures on energy
consumption firstly by keeping fixed thermostat and then adaptive thermostat settings.
Energy consumption of this model was taken as the reference for calculation of energy saving
which is referred by „as is case‟ in this study.
2.4.1. Control type
Part 1: fixed thermostat control
The thermostat setting of air conditioner was kept at 24oC constant throughout the year, since
it is a prevailing practice in India and then, seven envelope measures were used to evaluate
energy conservation in three warm climatic conditions.
Part 2: monthly variable thermostat control (adaptive thermostat control settings)
Under this part of the study thermostat of air conditioner was varied based on monthly
variation of outdoor temperature. The temperature at which occupants feel thermally
comfortable is a function of outdoor temperature and therefore thermostat of air conditioner
was varied as per the monthly varying neutral temperature reflecting thermal adaptation. It
was calculated using Equation-1 as suggested by Auliciems and de Dear [17].
Tn = 0.31To + 17.6 (equation 1)
Where-Tn is the neutral/comfort temperature. Neutral temperature or comfortable temperature
is worked out through regression analysis of occupant‟s thermal sensation vote. Regression
line that intersect at neutral condition („0‟ condition) on thermal sensation scale is defined as
neutral temperature and at this temperature majority of occupants feel thermal comfortable.
2.4.2. Energy conservation measures
This study considers seven envelope measures to evaluate the energy efficiency of air
conditioned building block. Five measures are recommended by ECBC and rest of the two
measures have been chosen based on their performance such as combination of
ECBC Glass + ECBC Roof, and ECBC case. Table 2 exhibits the details of recommended
measures by building code (ECBC) such as U-value for wall, roof, and glazing and SHGC of
glass, and reflectivity of roof in warm climates [18]. Table 3 shows the nomenclature of
envelope measures used in this study. Envelope measure 7 is also termed as ECBC case and
it follows all envelope measure recommended by ECBC.
6
Table 2 Recommended energy conservation measures
Cool
Roof reflectance
Wall U-value
(W/m2-oC)
Roof U-value
(W/m2-oC)
Glass U-value
(W/m2-oC)
Solar Heat Gain
Coefficient (SHGC)
0.70 0.440 0.261 3.30 0.25
Similar analysis was carried out in three different warm climatic zones of India namely
composite zone, hot & dry zone, and warm & humid zone represented by Hyderabad,
Ahmedabad, and Chennai respectively.
Table 3 Nomenclature of recommended energy conservation measures
Measures Nomenclature Name of energy conservation measure
As is case As is case Actual buildings case or existing case
ECM1 C R Cool Roof
ECM2 W ECBC wall
ECM 3 R ECBC Roof
ECM4 G S ECBC Glass SHGC
ECM5 G U ECBC Glass U-value
ECM6 R S ECBC Glass SHGC + ECBC Roof
ECM7 E all ECBC Case (1+2+3+4+5)
2.5. Sensitivity analysis of buildings block
Effect of a particular envelope measure also depends upon building type, building envelope,
internal load, occupancy schedule, type of air conditioning system, and operating conditions
etc, therefore it is required to carry out sensitivity analysis of employed ECMs. It has been
carried out considering large building area (square foot print of the building) and variation in
internal loads. It was carried out for the ECBC case only.
The effect of adaptive set point has therefore, been examined for different cases of Lighting
Power Densities (LPD), and Equipment Power Densities (EPD), as presented in Table 9. In
order to consider the variation in building size, that governs the role of building envelope in
the total cooling requirement, the analysis is further carried out in two parts; in the first part,
only the building size was changed to observe the impact of change in the exposed surface
area of building with respect to its volume. Size of the building block was increased from
3.6x2.4 m (8.6m2) to 40x40m (1600m
2). In the next variation, higher values of LPD and EPD
have been taken into consideration. The LPD was increased from 4W/m2 to 12 W/m
2 (as
suggested by ECBC for office buildings). The EPD was increased from 5W/m2
to 20W/m2
(as
found in IT offices).
7
Fig.1. Geographic location of hostel building (A), elevation of hostel building (B),
positioning of sensor at roof surface (C), suspended room air temperature probe (D),
CR 1000 data logger and connections of sensors (E), plan of simulation model (F), and
axonometric view of simulation model (G)
Fig.2. Layer by layer construction of roof and wall
8
3. Results
3.1. Temperature measurements
The average temperature difference between measured and simulated roof inside surface
temperature was observed 1.2oC whereas this difference for room air was found 1.1
oC. It was
observed that simulation roof inside surface temperature and room air temperatures were
found in good congruence with the onsite measurements, this ensured to proceed for further
analysis. Fig.3 and Fig.4 shows the variation of simulated and measured temperatures.
Fig.3. Variation of simulated and measured room air temperature
Fig.4. Variation of simulated and measured roof inside surface temperature
3.2. Validation of simulation model
Based on hourly simulation outputs such as room air temperature and roof inside surface
temperature, percentage Mean Bias Error (MBE) and Coefficient of Variation Root Mean
Square Error CV(RMSE) were calculated. These errors for roof inside surface temperature
and room air temperature were found less than 10% and 15 as shown in Table 4. Then, this
9
simulation model is called „validated simulation model‟.
Table 4 MBE (%) and Cv (RMSE), prior and post comparison of temperature
Inside roof surface Temp Room air temp
Prior
comparison
Post
comparison
Prior
comparison
Post
comparison
MBE (%) +14.09 + 4.06 +14.26 + 3.01
CV(RMSE) 22.52 13.94 18.20 7.55
3.3. Energy efficiency in representative climates
Energy efficiency of building block was improved by employing ECBC measures
considering fixed and adaptive control of thermostat. International Weather Energy
Calculation (IWEC) files were used to perform year round simulation of building block for
Ahmedabad and Chennai climatic locations. Indian Society of refrigerating and air
conditioning engineers (ISHRAE) weather file was used for Hyderabad because of
unavailability of IWEC file for this city. Weather files had hourly data of solar radiation,
outdoor temperature, relative humidity, wind velocity, sky conditions etc. Weather files were
not modified in this study. Table 5 shows the monthly variation of outdoor dry bulb
temperature and corresponding variation in neutral temperature in the representative cities of
warm climatic conditions. The maximum neutral temperature was noted down as 28oC in hot
and dry climate. The maximum thermostat temperature difference was observed 4oC in hot
and dry climate and this difference could lead to significant energy savings.
Table 5 Monthly outdoor dry bulb temperature and neutral temperature
Month Hot and dry
(Ahmedabad)
Warm and
humid (Chennai)
Composite
(Hyderabad)
Tmmo Tn Tmmo Tn Tmmo Tn
Jan 19.91 23.77 24.47 25.19 22.79 24.67
Feb 22.33 24.52 26.02 25.67 25.19 25.41
Mar 28.11 26.31 27.84 26.23 29.19 26.65
Apr 31.48 27.36 30.05 26.92 31.71 27.43
May 33.62 28.02 32.08 27.55 32.91 27.80
Jun 33.17 27.88 31.01 27.21 28.59 26.46
Jul 29.58 26.77 30.25 26.98 26.78 25.90
Aug 28.21 26.34 29.30 26.68 25.69 25.56
Sept 28.86 26.55 29.02 26.59 26.19 25.72
Oct 27.19 26.03 27.72 26.19 26.11 25.69
Nov 23.53 24.89 26.07 25.68 23.71 24.95
Dec 20.56 23.97 24.80 25.29 21.74 24.34
3.3.1. Energy efficiency in composite climate
Hyderabad was chosen as representative city for composite climate while analyzing the effect
of thermal adaptation, the cooling set point is varied on monthly basis as per the neutral
temperature that changes from 26.6oC during March to 27.8
oC during the month of May. It is
observed that neutral temperature has significant difference with constant thermostat (24oC)
10
as shown in Fig.5. The variation of temperature in this climate ranges from 4 to 43oC and
relative humidity varies from 20 to 95% (dry period to wet period).
Fig.5. Monthly variation of neutral temperature and mean monthly outdoor dry bulb
temperature (Composite climate, Hyderabad)
Table 6 shows the annual energy consumption considering seven measures using fixed and
adaptive set point for the HVAC system. Following assertions are noted from the results:
With ECM 7, i.e. combination of all individual ECMs termed as ECBC case, 40%
energy could be saved over the common practice case i.e. the „as is‟ case.
Further, additional energy savings by about 15 to 19% could be achieved (maximum
of 30kWh/m2/yr) by using adaptive set point conditions.
The energy savings with various ECMs with adaptive set point approach are of the
same order as compared to the cases with fixed set point approach. This is evident
from comparison of Figure 6 and 7. This indicates that with adaptive set point
approach, the suggested ECMs have nearly the same importance.
From Figure 6 & 7, it can be observed that in the ECBC case and with adaptive
approach, the monthly variation of energy consumption reduces significantly, whereas
in case of fixed set point conditions peak is very high as compared to rest of the
period.
Fig.6 and Fig. 7 revealed that adaptive approach has large energy savings opportunities in
composite climate throughout the year. It is also evident that the maximum energy saving is
possible from March to June. ECBC case (ECM_all) shows the lowest energy consumption
compare to other envelope measures.
11
Fig.6. Energy consumption in Hyderabad considering fixed set point conditions
Fig.7. Energy consumption in Hyderabad considering adaptive set point conditions
Table 6 Energy consumption at both set points conditions in composite climate
Annual Energy Saving in Case of Hyderabad
cases Energy
consumption
Fixed Set point
(kWh/m2yr)
Energy consumption
Adaptive Set point
(kWh/m2yr)
Actual Energy
Saving
(kWh/m2yr)
Percentage
saving
(%)
As is case 177.89 149.34 28.54 16.04
ECM_1 164.23 135.63 28.59 17.41
ECM_2 142.52 117.12 25.39 17.82
ECM_3 141.09 115.83 25.25 17.90
ECM_4 156.01 125.99 30.01 19.24
ECM_5 184.78 155.27 29.52 15.97
ECM_6 141.37 116.39 24.99 17.67
ECBC case 105.69 89.20 16.49 15.60
12
3.3.2. Energy efficiency in hot and dry climate
For hot and dry climate, Ahmedabad was chosen as representative city. While analyzing the
effect of thermal adaptation, the cooling set point is varied on monthly basis as per neutral
temperature that changes from 26.3oC during March to 28.02
oC during the month of May.
Figure 8 shows the variation of adaptive thermostat and constant thermostat. The variation of
mean monthly outdoor dry bulb temperature is large (20 to 38oC) in this climate and relative
humidity varies from 25 to 40%.
Fig.8. Monthly variation of neutral temperature and mean monthly outdoor dry bulb
temperature (hot and dry climate, Ahmedabad)
It is observed that there is a significant difference between neutral temperature and constant
thermostat compare to composite climate because of harsh summers and winters conditions.
Table 7 shows the annual energy consumption per unit area considering each ECM using
fixed and adaptive set point for the HVAC system. Following conclusions are noted down
from the results:
With ECM 7, i.e. combination of all individual ECMs (ECBC case), 43.1% energy
could be saved over the common practice case i.e. the „as is‟ case.
Further, additional energy saving by about 15 to 19% could be achieved (maximum of
33kWh/m2/yr) by using adaptive set point condition.
The effect of ECMs with adaptive set point approach is similar as compared to the
fixed set point approach. This is evident from comparison of Figure 9 and 10.
From Figure 9 & 10, it can be observed that in ECBC case and with the adaptive
approach, the monthly variation of energy consumption reduces significantly, whereas
in case of fixed set point conditions, peak is very high as compared to the rest of the
period.
13
Fig.9. Energy consumption in Ahmedabad considering fixed set point conditions
Fig.10. Energy consumption in Ahmedabad considering adaptive set point conditions
It is observed form Fig.9 and Fig. 10 that roof and wall insulation shows large energy savings
potential considering fixed and adaptive set point conditions. The peak specific energy
consumption of ECBC case has also reduced to a great extent in case of adaptive approach.
Table 7 Energy consumption at both set point conditions in hot and dry climate
Annual Energy Saving in case of Ahmedabad
cases Energy consumption
Fixed Set point
(kWh/m2yr)
Energy consumption
Adaptive Set point
(kWh/m2yr)
Actual Energy
Saving
(kWh/m2yr)
Percentage
saving
(%)
As is case 196.29 164.52 31.77 16.18
ECM_1 181.49 149.79 31.71 17.47
ECM_2 161.53 136.41 25.12 15.55
ECM_3 156.50 129.15 27.35 17.48
ECM_4 179.45 146.37 33.07 18.43
14
ECM_5 200.95 168.24 32.71 16.28
ECM_6 155.46 128.26 27.20 17.50
ECBC Case 111.69 93.24 18.44 16.51
3.3.3. Energy efficiency in warm and humid climate
Chennai is chosen as representative city for warm and humid climate. While analysing the
effect of thermal adaptation, the cooling set point is varied on monthly basis as per the neutral
temperature that changes form 26.2oC during March to 27.5
oC during the month of May.
Figure 11 shows that there is not much difference between neutral temperature and constant
thermostat line due to less variation in climatic conditions round the year. The variation of
dry bulb temperature ranges from 20 to 35oC whereas relative humidity is all-time high such
as 70 to 90%.
Fig.11. Monthly variation of neutral temperature and mean monthly outdoor dry bulb
temperature (warm and humid climate, Chennai)
Table 8 demonstrates the annual energy consumption per unit area considering each ECM
using fixed and adaptive set point for the HVAC system. Following observations are noted
down such as:
With ECM_7, i.e. combination of all individual ECMs (ECBC case), 39% energy
could be saved over the common practice case i.e. the „as is‟ case.
Further, additional energy saving by about 15 to 19% could be achieved (or maximum
of 36.6kWh/m2/yr) by using adaptive set point condition.
The effect of ECMs with adaptive set point approach is similar as compared to the
fixed set point approach. This is clear from comparison of Fig. 12 and Fig. 13.
From Fig. 12 & 13, it can be revealed that when all the ECMs applied with adaptive
approach, the monthly variation of energy consumption reduces by a large extent,
whereas in case of fixed set point conditions peak is very high compared to rest of the
period.
15
Fig.12. Energy consumption considering fixed set point conditions in Chennai
Fig.13. Energy consumption considering adaptive set point conditions
Figure 12 and 13 shows that there is a large potential of energy savings between fixed and
adaptive set points conditions. There is less variation in weather conditions in the chosen
climate, specific energy consumption of ECBC case is more than other climates and the
maximum saving is possible during May only.
Table 8 Energy consumption at both set points conditions in warm and humid climate
Annual Energy Saving in case of Chennai
cases Energy consumption
Fixed Set point
(kWh/m2yr)
Energy consumption
Adaptive Set point
(kWh/m2yr)
Actual Energy
Saving
(kWh/m2yr)
Percentage
saving
(%)
“As is”
case 212.37 177.64 34.72 16.35
ECM_1 197.45 162.50 34.95 17.70
ECM_2 179.63 151.63 28.01 15.59
16
ECM_3 172.09 141.83 30.26 17.59
ECM_4 195.31 158.67 36.64 18.76
ECM_5 217.99 181.99 36.00 16.51
ECM_6 171.45 141.19 30.26 17.65
ECBC case 128.80 108.14 20.66 16.04
3.4. Sensitivity analysis
Analysis of variation of building size reveals that with increase in building size keeping the
intensities of internal loads constant, the energy saving due to ECBC measures reduces from
39 % to 15.9 % in Chennai, from 40.6 % to 28.6% in Hyderabad, and from 43.09 % to 16.7
% in Ahmedabad.
The effect of thermal adaptation in large buildings reduces significantly from 16 % to 10.5 %
in Chennai, 16.8 % to 6.3 % in Hyderabad, and from 16.7 % to 6.2% in Ahmedabad.
Similarly, analysis of change in internal load shows that effect of thermal adaptation gets
reduced further from 10.5 to 3.4 in Chennai, 6.2 to 2% in Ahmedabad and from 6.3 to 2.5%
in Hyderabad. Table 9 illustrates the variation in internal load and corresponding energy
savings in chosen climates. It is concluded from Table 9 that 27% energy saving is possible
considering small buildings with high internal loads in hot and dry climate whereas Table 10
demonstrates that energy savings reduces as increase in building size and internal loads (high
internal load).
Thus, sensitivity analysis reveals that the effect of adaptive set point gets reduced in large
building blocks however in all cases, considering thermal adaptation is important to estimate
the actual behaviour of unconditioned buildings and for estimating energy savings in building
with air conditioning.
Table 9 Energy consumption and energy savings potential at different internal loads
Variation
of LPD
& EPD
(W/m2)
Fixed set point conditions
Hyderabad Ahmedabad Chennai
„As is'
case
ECBC
Case
Savin
gs (%)
„As is'
case
ECBC
Case
Savin
gs (%)
„As is'
Case
ECBC
Case
Saving
s (%)
LPD 10
EPD 10
199.3 168.8 15.3 299.2 173.3 42.1 270.0 189.1 30.0
LPD 10
EPD 15
222.8 207.8 6.7 321.2 212.1 34.0 295.4 228.5 22.6
LPD 12
EPD 20
246.6 246.5 0.0 343.7 250.6 27.1 320.6 267.7 16.5
Table 10 Summary of results –variation analyzed under representative cities
Variation for
sensitivity analysis
Hyderabad Ahmedabad Chennai
% Energy savings with
ECBC Case over „as is‟
case at fixed thermostat set
point conditions
Small building 40.6 43.09 39.35
Large building with
low LPD/EPD
28.21 32.7 30.9
Large building with
high LPD/EPD
- - -
17
% Energy savings with
ECBC Case over „as is‟
case at adaptive thermostat
set point conditions
Small building 40.3 43.33 39.12
Large building with
low LPD/EPD
6.3 6.6 10.5
Large building with
high LPD/EPD
2.5 2.2 3.4
3.5. Summary and discussion
It is observed that buildings complying with the Energy Conservation building Code of India,
may consume about 40% less energy as compared to building built with conventional
construction practices of India. Table 11 summarizes the results of above analysis which is
carried out for three different cities under different climatic zones. Maximum energy savings
is possible in hot and dry climate as there is large variation in weather conditions. The
maximum annual energy consumption („as is case‟ 212 kWh/m2yr) was found in warm and
humid climate being similar variation in weather conditions round the year whereas minimum
annual energy consumption (89 kWh/m2/yr) was observed in composite climate considering
adaptive thermostat settings. Therefore, result reveals that composite climate is much
appropriate for evaluating the effect of thermal adaptation due to moderate change in climatic
conditions.
Table 11 Energy consumption considering fixed and adaptive set point conditions in
respective cities
Composite climate (Hyderabad)
cases Energy consumption
Fixed Set point
(kWh/m2/yr)
Energy consumption
Adaptive Set point
(kWh/m2/yr)
Energy
Saving
(kWh/m2/yr)
Saving
(%)
„As is‟ case 177.89 149.34 28.54 16.04
ECBC case 105.69 89.20 16.49 15.60
Saving % 40.6 40.3 42.2 -
Hot and Dry climate (Ahmedabad)
cases Energy consumption
Fixed Set point
(kWh/m2/yr)
Energy consumption
Adaptive Set point
(kWh/m2/yr)
Energy
Saving
(kWh/m2/yr)
Saving
(%)
„As is‟ case 196.29 164.52 31.77 16.18
ECBC case 111.69 93.24 18.44 16.51
Saving % 43.09 43.33 42.0 -
Warm and Humid climate (Chennai)
cases Energy consumption
Fixed Set point
(kWh/m2/yr)
Energy consumption
Adaptive Set point
(kWh/m2/yr)
Energy
Saving
(kWh/m2/yr)
Saving
(%)
“As is” case 212.37 177.64 34.72 16.35
ECBC case 128.80 108.14 20.66 16.04
Saving % 39.35 39.12 40.5 -
Sensitivity analysis shows that, energy savings gets reduced to 16% with increase in building
size and internal loads. The effect of thermal adaptation in large buildings reduces
significantly from 16 % to 10.5 % in warm and humid climate (Chennai), from 16.8 % to 6.3
% in composite climate (Hyderabad), and from 16.7 % to 6.2% in hot and dry climate
18
(Ahmedabad). Similarly, analysis of change in internal load illustrates that effect of thermal
adaptation gets reduced further from 10.5 to 3.4 % in Chennai, 6.3 to 2.5% in Hyderabad and
from 6.2 to 2% in Ahmedabad. It is observed that large building with low internal load gives
energy savings of about 28 to 32% considering constant thermostat conditions which reduces
to 6 to 10% considering thermal adaptation. It is concluded that ECBC envelope improves the
energy performance of a building although specific measure should be chosen wisely as all
the ECMs do not offer same energy performance in all climates.
4. Conclusion
This study evaluates improvement in energy efficiency of an air conditioned building block
employing energy conservation measures recommended by National Energy Conservation
Building Code (ECBC). Following are the key conclusion of the study-
- Small building with ECBC specifications gives energy saving opportunity of about
43% compared to buildings built with conventionally practiced specifications of
India.
- The effect of thermal adaptation itself offers up to 16% energy conservation through
adaptive thermostat settings changing as per mean monthly outdoor temperature.
- However, in case of large buildings having high internal heat gain resulting from
lighting, equipment, occupancy; energy savings due to adaptive thermostat get
reduces to negligible amount.
- The effect of thermal adaptation is of the same order for buildings constructed with
common practices and buildings having specifications as per ECBC.
Study suggests implementation of recommended envelope measures of building code to
improve energy efficiency in warm climatic conditions. Study highly recommends the use of
roof insulation over other ECMs except ECBC case. This measure alone offers 20% energy
savings whereas group of other envelope measures gives 40% energy savings opportunities.
Wall insulation also put forward significant energy conservation. Combination of roof and
glass (ECBC roof + Glass SHGC) measure has not been found much effective over roof
although it is recommended over wall insulation. This study also suggests use of adaptive
thermostat control to reduce additional 16% energy consumption over fixed thermostat. Use
of envelope measures along with adaptive thermostat concept is highly recommended.
This, study would be useful to facility managers, investor, architects, engineers, and
contractors to choose the appropriate envelope measures in particular climate and to operate
air conditioner on monthly variable thermostat settings to provide the most comfortable
environment.
Acknowledgement
We thank to Prof. Andreas Wagner and Dr. Marcel Schweiker from Department of Building
Physics and Building Services (fbta), Karlsruhe Institute of Technology Karlsruhe, Germany
for their help during revision of this paper.
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