Thermal performance and embodied energy analysis of a passive house – Case study of vault roof mud-house in India

Applied Energy 86 (2009) 1956–1969

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Thermal performance and embodied energy analysis of a passive house – Case study of vault roof mud-house in India Arvind Chel *, G.N. Tiwari Center for Energy Studies (CES), Indian Institute of Technology Delhi, Block V, Hauz Khas, New Delhi 110 016, India

a r t i c l e

i n f o

Article history: Received 10 October 2008 Received in revised form 23 December 2008 Accepted 28 December 2008 Available online 5 February 2009 Keywords: Vault roof Embodied energy Energy saving Mitigation of CO2 Carbon credit

a b s t r a c t This paper investigates thermal performance of an existing eco-friendly and low embodied energy vault roof passive house (or mud-house) located at Solar Energy Park of IIT Delhi, New Delhi (India). Based on embodied energy analysis, the energy payback time for the mud-house was determined as 18 years. The embodied energy per unit floor area of R.C.C. building (3702.3 MJ/m2) is quiet high as compared to the mud-house (2298.8 MJ/m2). The mud-house has three rooms with inverted U-shape roof and remaining three rooms with dome shape roof. A thermal model of the house consisting of six interconnected rooms was developed based on energy balance equations which were solved by using fourth order Runge Kutta numerical method. The predicted six room air temperatures were found in good agreement with the experimental observed data on hourly basis in each month for one year. The annual heating and cooling energy saving potential of the mud-house was determined as 1481 kW h/year and 1813 kW h/year respectively for New Delhi composite climate. The total mitigation of CO2 emissions due to both heating and cooling energy saving potential was determined as 5.2 metric tons/year. The annual carbon credit potential of mud-house was determined as € 52/year. Similar results were obtained for the different climatic locations in India. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction The passive solar house is an ancient concept in architecture and describes a way to design a building giving due consideration to the site, climate, local building materials and the position of the Sun at the site of building construction. Now, there is a renewed interest in passive solar home design for obtaining natural heating and cooling of room air using the energy available in the immediate environment. The passive house made of mud is one of the solutions for rural habitat building constructions to conserve the energy both during construction as well as for achieving thermal comfort inside the house. Coffman et al. [1] reported that the mud-house construction have natural air conditioning effect because the rooms are cool during daytime and warm during nighttime. The application of mud as wall material was investigated to control room air temperature for buildings by Duffin and Knowles [2]. The most common passive solar building architecture comprises of massive walls to reduce the temperature fluctuations inside a building. This is known as the thermal flywheel effect as mentioned by Duffin

* Corresponding author. Tel.: +91 9968144689; fax: +91 011 26581121. E-mail address: [email protected] (A. Chel). 0306-2619/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2008.12.033

and Greg [3]. The popular mud-houses in Yemen city utilize this effect. The use of mud as building material is of great concerns not only for the people in hot developing countries, but also for those in cold industrialized countries in Europe and America [4]. The engineers from developed countries have realized the special features of mud as reported by Eben [4]. The wider use of mud construction has a good reputation in dry and hot places because of its distinct advantages, e.g. the mud habitat suits different weather and geographical conditions as the temperature remains temperate throughout the year inside the mud building as explained by Eben [4]. Algifri et al. [5] compared the thermal behavior of adobe house with modern concrete house in Yemen and reported the potential of mud as construction material for energy saving in passive houses. The magic of adobe houses was investigated by Miller [6]. Earth as mud bricks, has been used in the construction of shelters for thousands of years, and approximately 30% of the world’s present population still live in earthen structures as reported by Cofirman et al. [7]. Earth is a cheap, environmental friendly and abundantly available building material. It has been used extensively for wall construction around the world, particularly in developing countries reported by Ren and Kagi [8]. Binci et al. [9] investigated that home brick-makers of Turkey and the Middle East are using fibrous ingredients like straw to improve the tensile

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

1957

Nomenclature A ARoof AWall AWindow Ca e h ho hi Id Ig IT K L Ma N n Q Gain Q Wall Q Roof Q Window Q Door Q Internal Q Person Q Computer Q Lamp Q Freezer Q Loss Q Ground Q Isothermal Q Ventilation r t Ta Tr1 Tr2 Tr3

area of wall/roof surface (m2) area of roof (m2) area of wall (m2) area of window (m2) specific heat of room air (J/kg K) root mean square percentage deviation (%) time interval (h) outside heat transfer coefficient of wall/roof surface (W/ m2 K) inside heat transfer coefficient of wall/roof surface (W/ m2 K) diffuse solar radiation on horizontal surface (W/m2) global solar radiation on horizontal surface (W/m2) solar radiation available on inclined surface (W/m2) thermal conductivity of material (W/m K) thickness of material layer (m) mass of room air (kg) number of air change per hour (h1) number of observations heat gain into the room (W) heat gain/loss through wall surfaces (W) heat gain/loss through roof surfaces (W) heat gain/loss through window (W) heat gain/loss through door (W) internal heat gain (W) heat gain from human body (W) heat gain from computer (W) heat gain from lamp (W) heat gain from freezer (W) heat loss from room air to outside ambient air (W) heat loss into the ground (W) heat loss to the isothermal mass e.g. furniture, cupboards etc. (W) heat loss from room air to outside ambient air by ventilation (W) coefficient of correlation time interval (h) ambient air temperature (°C) room-1 air temperature of mud-house (°C) room-2 air temperature of passive house (°C) room-3 air temperature of passive house (°C)

strength of mud bricks for millennia. Binici et al. [9] had investigated the thermal isolation and mechanical properties of fibre reinforced mud bricks as wall materials. Stabilized mud blocks are made in India from soil, sand, cement/lime and water. These blocks are used for making vaulted roof structure buildings as shown in Fig. 1 as reported by Reddy [10]. Roofing systems using brick masonry vaults and domes were used extensively in India till the emergence of British rule. It has several advantages as compared to reinforced slab roofing as reported by Reddy [10]. An innovation in vault was developed by Reddy [10] using a moving steel formwork (slip forming) which facilitates construction without excessive cost and time out run. Currently, catenary shaped vaults are being built due to their better performance. Both burnt bricks and stabilized mud blocks were used for building vaults and domes. More than 30 buildings were built since 1986 in and around Bangalore city in India using vaults and domes as reported by Reddy [10]. Recently, the advantage of mud-houses reported by Collet et al. [11] are reduction of energy consumption, green house gases emission, water use, waste production, etc. Thermal behavior of such

Tr4 Tr5 Tr6 T ra T sol U Va

room-4 air temperature of passive house (°C) room-5 air temperature of passive house (°C) room-6 air temperature of passive house (°C) room air temperature of adjacent room (°C) sol–air temperature of the Sun exposed surfaces (°C) overall heat transfer coefficient of wall/roof structure (W/m2 K) room air volume of mud-house (m3)

Greek symbols absorptivity of surface b slope of wall/roof surface with respect to horizontal sg transmissivity of window/ventilator glass e emissivity of surface qa density of air (kg m3)

a

Subscripts a air exp t experimental g glass pred predicted r room 1 room-1 2 room-2 3 room-3 4 room-4 5 room-5 6 room-6 Abbreviations AES annual energy saving potential of renovated passive house (kW h/year) D door EPBT energy payback time (years) EER embodied energy for renovation (kW h) RCC reinforced cement concrete structure TEE total embodied energy of renovated passive house (kW h) W window V ventilator

low energy consuming and low carbon building having clay wall facing south was investigated by Collet et al. [11]. Hence, such mud-houses needs to be promoted especially in rural areas for the sustainable development of the environment. Pearlmutter [12] had made the first attempt to quantitatively compare thermal behavior of vaulted and flat roofs in terms of indoor temperatures. It was shown that the vaulted roof has greater thermal stability and potentially favorable daytime temperature. Hadavand and Yaghoubi [13] had reported that the convection coefficient over vaulted roof is significantly higher on the forward side and decreases after separation on the leeward side, but its variation for flat roof is not considerable. The temperature varies along the vaulted roof during the day as a result of the auto shading of roof but for flat roof temperature distribution along the roof is nearly uniform as reported by Hadavand and Yaghoubi [13]. The numerical calculation performed by Tang et al., [14] showed that vaulted roof buildings have lower indoor air temperatures as compared to flat roof. The reason is curved roof structures dissipate more heat as compared to flat roof by convection and

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A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

Fig. 1. Vaulted roof building made of stabilized mud blocks (composition: soil, sand, lime/cement and water) reported by Reddy[10].

thermal radiation at night due to the enlarged surface area. Similarly, in this paper numerical solution of six first order linear differential heat balance equations was obtained using Runge Kutta fourth order numerical method. These numerical solutions were used to formulate the thermal model of the existing mud-house (or passive house) to predict hourly values of room air temperatures of six interconnected rooms. The predicted room air temperatures were found in good agreement with experimental data obtained on typical clear day in winter and summer months in New Delhi. Similar to the methodology of evaluation of energy saving calculations presented by Chel and Tiwari [15], the annual heating and cooling energy saving potential of mud-house was evaluated. This annual energy savings potential of passive house results in mitigation of CO2 emission which was estimated similar to the equation presented by Chel and Tiwari [15]. The potential of earning carbon credits due to CO2 mitigation from the energy saving potential was calculated and presented in this paper for different cities in India. Building construction has a major role on the environment because of energy consumption for acquiring building raw materials and generation of waste during construction. Building construction indirectly emits greenhouse gases as waste. Since, environmental issues have become serious and significant; there is need for more energy efficient buildings to cut down its energy needs for operation Dimoudi and Tompa [16]. Thus, the energy required for construction and consequently for the building material production has gained greater importance nowadays. The present paper investigates the role of different construction materials and quantifies them in terms of the embodied energy and the equivalent emissions of CO2 emissions from passive buildings as compared to cement concrete buildings. It also assesses the importance of the low embodied energy building materials such as mud for thermal comfort inside the building by estimating the energy saving potential of the mud-house in India. The embodied energy and life cycle energy consumption were reported as key parameters for building energy assessment as reported by Casalas [17]. But these parameters are often left out of the regulation and certification proposals as reported by Casalas [17]. Hence, in this paper, the embodied energy analysis of the passive house (or mud-house) was determined and compared with results obtained by Gumaste [18] for reinforced cement concrete structure. The energy payback time for the passive house was determined and presented at the end of this paper. The input climatic data for Indian cities, e.g. solar radiation (global and diffuse) and ambient air temperature were obtain from Indian Meteorological Department (IMD), Pune which divides every month into four weather types (a–d). The thermal performance of mud-house structure was rarely reported and compared with another type of building in the literature. Hence, the present work on embodied energy analysis and thermal performance of mud-house reveals the importance of this ecofriendly and sustainable buildings in India and all over the world.

2. Description of existing mud-house (or passive house) The building design details, wall and roof composition of the passive house and the surface area of different building components are given below. 2.1. Mud-house wall and roof material and their details The passive house is shown in Fig. 2a was constructed from two major materials, adobe (or mud) and stabilized mud block bricks. There is no steel reinforcement like RCC roof for the passive house since this is load bearing brick vault structure built in the year 1984 by German architect. The construction materials used for the renovated passive house are as shown in Fig. 2b. The passive house roof structure was retrofitted with brick-tile layer. This renovated roof structure of passive house consists of inside mud plaster (4%), stabilized mud block bricks (41%), mud layer (41%) and outer brick-tiles layer (14%) from Fig. 2b. The major construction material used is ‘mud’ and hence this passive house is known as mud-house. All the construction details and thermal properties of materials used are mentioned in Table 1a–1d. The old roof structure of passive house was renovated using an additional wet mud layer of 1 cm thickness and press fitted brick-tiles layer of thickness 3.75 cm. The existing passive house has roof thickness of 27 cm and wall thickness of 35 cm as mentioned in Table 1b. The spacing between brick-tiles is filled with mixture of cement mortar and water proofing chemical which is commercially known as Dr. Fixit. It is experimentally observed that after the renovation, this passive house is free from the problems of top surface mud loss during rain and water seepage in to the building. Also, there is improvement in thermal performance of passive house due to renovation. 2.2. History and design details of existing mud-house rooms This passive house has brick vault roof structures both inverted U-shaped and dome shaped structures as shown in Fig. 2a–d. This house was designed by German architect under Indo-German project in the year 1984 located at Solar Energy Park, IIT Delhi. This house is used as simulation research laboratory for the scientists working on solar energy utilization from last 24 years without any kind of major damage due to environmental obstructions since it is a stable load bearing brick vault structure. There are total six rooms in this passive house, out of which three rooms are facing east side having inverted U-shape roof structure as shown in Fig. 2a and c and remaining three rooms are located on west side with dome shaped roof structures as shown in Fig. 2c and d. These six rooms are interconnected with each other using internal wooden doors of area 2 m2. The design details are shown in Fig. 2e and presented in Table 1a–1c. The overall heat transfer coefficient values (or U-values) of all building components are given in Table 1a–1d. The three east facing rooms like south room (or store room), middle room (or cross ventilated

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

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Fig. 2. (a) Vaulted roof passive house made of brick vault, mud layer and outer brick-tiles (b) Composition of roof construction materials (volume basis). (c) Schematic diagram of east facing front view of passive house. (d) Schematic diagram of west facing rear view of passive house. (e) Schematic diagram of three inverted U-shape rooms. (f) Schematic diagram of inverted U-shaped roof structure made of seven surfaces.

room) and north room (or simulation room) were numbered as 1, 2 and 3, respectively. The remaining three dome shape rooms are numbered as 4, 5 and 6. The room-1 is connected with room-2

by the door passage of area 2 m2 which is closed by an internal brick wall layer of thickness 11.25 cm. The room-2 is connected with room-3 using plywood internal door of thickness 15 cm and

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Table 1a Design details and thermal properties for thermal model of mud-house. No.

Parameter

Value

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Convective heat transfer coefficient (outside room) Convective heat transfer coefficient (inside room) Thermal conductivity of mud Thermal conductivity of brick Thermal conductivity of plywood Thermal conductivity of glass Thermal conductivity of steel (used for door) Steel door thickness Plywood door thickness Transmissivity of glass Glass thickness of window and ventilator Absorptivity of surface Emissivity of surface Air change per hour Volume of each inverted U-shaped room (1,2 and 3) Volume of each small dome shaped room (4 and 6) Volume of big dome shaped room (5) Specific heat of room air Area of each wooden door and internal door Area of each glass window (on east and west wall) Area of each glass ventilator (on east and west wall) Total floor area of mud-house six rooms

23 W/m2 K 6 W/m2 K 0.519 W/mK 0.72 W/mK 0.174 W/mK 1 W/mK 50 W/mK 1.5 mm 15 cm 0.9 3 mm 0.6 0.9 2 h1 63 m3 15 m3 160 m3 1005 J/Kg K 2 m2 1.2 m2 1.34 m2 94 m2

area 2 m2. Also, the middle room-2 is connected with the big dome shape room-5 using plywood internal door of thickness 15 cm and area 2 m2 on west side. This big dome shaped room-5 is connected to two small dome shaped rooms (room-4 and room-6) using internal plywood doors of thickness 15 cm and area 2 m2 on south-west and north-west orientations. The inverted U-shape of rooms 1, 2 and 3 is divided into seven surfaces for ease in writing modeling heat balance equations for the respective room as shown in Fig. 2e and f. These three rooms are interconnected to each other as shown in Fig. 2e. The dome shape roof is divided in to eight walls and 24 roof surfaces as shown in Fig. 3a for big dome room-5. Similarly, small dome structures are divided into eight walls and 24 roof surfaces as shown in Fig. 3b.The complete plan view of the passive house is shown in Fig. 3b. The surface area of all building components of passive house is mentioned in Table 1d.

2.3.1. Ambient and room air temperature The ambient and room air temperature of passive house building is measured with the help of a calibrated mercury glass thermometer of 0.5 °C least count. 2.3.2. Solar radiation The diffuse and global solar radiations are measured using the two calibrated Pyranometers with and without shading ring, respectively which are connected to computer with ADAM data logger system (least count 0.1 W/m2). 3. Thermal modeling of passive house The assumptions involved in the thermal model of six interconnected rooms of passive house (or mud-house) are given below. 3.1. Assumptions There are following assumptions involved before developing the thermal model of six interconnected passive house rooms: (1) The heat transfer through the roof and walls occurs in one direction along the thickness. (2) The heat gain/loss through the roof and wall was assumed constant for every hour. (3) The wall and roof structures are made of homogeneous material layers. (4) The ambient and room air temperatures are assumed to be constant for every hour. (5) Solar intensity is assumed constant for one hour duration. (6) The values of parameters like air change per hour and inside (hi) and outside (ho) convective heat transfer coefficients are assumed constant. (7) All thermal properties of building materials e.g. thermal conductivity and specific heat are assumed constant and independent of temperature variations.

3.2. Energy balance equations for non-air-conditioned passive house rooms

2.3. Instruments used The following instruments have been used during the experimentation to measure the different parameters.

There are six interconnected passive house rooms. The energy balance equations for each room in general can be written as follows based on Chel and Tiwari [15]:

Table 1b Construction details of wall and roof structures of mud-house rooms. Material layers (inside to outside)

Mud plaster Mud brick Mud Brick-tile Mud plaster Total thickness (cm) U-value (W/m2 K)

For inverted U-shaped Rooms – 1, 2, 3

For dome shaped Rooms – 4, 5, 6

Wall thickness(cm)

Old roof thickness (cm)

Renovated roof thickness (cm)

Wall thickness (cm)

Old roof thickness (cm)

Renovated roof thickness (cm)

1 33.75 – – 1.25 36 1.39

1 11.25 10 – – 22.25 1.74

1 11.25 11 3.75 – 27 1.55

– 33.75 – – 1.25 35 1.43

– 11.25 10 – – 21.25 1.80

– 11.25 11 3.75 – 26 1.59

Table 1c U-value of building components. Building components

Outside plywood door

Internal plywood door

Outside mild steel door

Outside glass window, ventilator and skylight

Internal brick wall

Thickness (cm) U-value (W/m2 K)

15 0.94

15 0.84

0.15 4.83

0.3 4.22

11.25 1.84

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A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969 Table 1d Surface area of wall, roof, window, ventilator and door of mud-house rooms. Surface of inverted U-shaped roof house

Surface area (m2)

Surface of dome-shaped roof house

Surface area (m2)

East wall West wall A1 A2 A3 A4 A5 A6 A7 Window Ventilator Door

9.92 9.92 16.22 4.77 4.13 4.13 4.13 4.77 16.22 1.2 1.34 2

Wall (big dome) A11–A18 A21–A28 A31–A38 Triangular part of skylight glass pyramid Window Wall (small dome) A11–A18 A21–A28 A31–A38 Triangular part of skylight pyramid Door

5.38 5.77 2.35 0.72 0.32 1.2 1 1.07 0.44 0.14 0.15 2

X

dT r X ¼ Q Gain  Q Loss dt ¼ Q Wall þ Q Roof þ Q Window þ Q Door þ Q Internal

Ma C a

Q Gain

Q Loss ¼ Q Ground þ Q Isothermal þ Q Ventilation

ð1Þ ð2Þ ð3Þ

The expressions derived for rate of heat gain and loss from different components for quasi-steady state heat transfer analysis is given below: The rate of heat gain through wall is

Q Wall ¼ ðUAÞWall ðT sol;Wall  T r Þ  ðUAÞWall ¼

ð4Þ

1 L1 L2 1 þ þ þ  þ ho K 1 K 2 hi

1

T sol ¼

aIT

eDR

þ Ta  ho ho   cos b DR ¼  60 sin b

 ð5Þ ð6Þ

The rate of heat gain through roof is

Q Roof ¼ ðUAÞRoof ðT sol;Roof  T r Þ

ð7Þ

The rate of heat gain through window is

Q Window ¼ AWindow  s  IT þ ðUAÞWindow ðT sol;Window  T r Þ

ð8Þ

The rate of heat gain in room-1 through internal door from room-2

Q Door ¼ ðUAÞDoor ðT r2  T r1 Þ

ð9Þ

The rate of heat gain from internal heat generators

Q Internal ¼ Q Person þ Q Computer þ Q Lamp þ Q Freezer

ð10Þ

The typical values of internal heat gain from one person, one computer, one lamp and one freezer were assumed 60 W, 70 W, 40 W and 80 W, respectively. The rate of heat loss from room air is given by Eq. (3). The rate of heat loss through ground (QGround) was assumed zero, since the ground surface temperature is assumed equal to room air temperature all the time in quasisteady state condition. The heat exchange between isothermal mass and room air was neglected. The isothermal mass comprises of mass of furniture, cupboard, computer etc. The equation for rate of heat loss due to room air ventilation and/or infiltration to ambient air can be expressed as follows:

Q Ventilation ¼

qa V a C a NðT r  T a Þ 1

3600ðsh Þ

¼ 0:33NV a ðT r  T a Þ

X

 dT r1 Ma C a 1 dt 9 8 2 7 P P > > > > > > ½ðUAÞi ðT sol i  T r1 Þ þ ½ðUAÞj ðT sol j  T r1 Þ > > > > > > j¼1 = < i¼1 3 ¼ P > þ ½Ak sg ITk þ ðUAÞk ðT sol k  T r1 Þ þ ½ðUAÞDoor ðT sol East  T r1 Þ > > > > > > > k¼1 > > > > ; : þ½ðUAÞBrick ðT r2  T r1 Þ  qa V a ca NðT r1  T a Þ

or,

dT r1 ¼ f ðT r1 ; T r2 ; T r3 ; T r4 ; T r5 ; T r6 ; B1 ðtÞÞ dt

 AWall

The expression of sol–air temperature on any inclined wall/roof surface can be written as:



3.2.1. Heat balance equation for inverted U-shape room number-1

ð11Þ

Based on Eqs. (1)–(11), the heat balance equation for six passive house rooms can be written as follows:

or,

dT r1 ¼ a11 T r1 ðtÞ þ a12 T r2 ðtÞ þ a13 T r3 ðtÞ þ a14 T r4 ðtÞ þ a15 T r5 ðtÞ dt þ a16 T r6 ðtÞ þ B1 ðtÞ

ð12Þ

The constants a11, a12, a13, a14, a15 and a16 are the coefficients of room air temperatures of rooms 1, 2, 3, 4, 5 and 6, respectively. The term B1(t) represents the function of time ‘t’ in Eq. (12) for room-1 and comprises of the time dependent terms like sol-air surface temperature (Tsol), ambient air temperature (Ta) and solar radiation on tilted surface (IT). Similarly, the heat balance equations for remaining inverted U-shape roof rooms 2 and 3 can be expressed as follows: 3.2.2. Heat balance equation for room number-2

dT r2 ¼ b11 T r1 ðtÞ þ b12 T r2 ðtÞ þ b13 T r3 ðtÞ þ b14 T r4 ðtÞ þ b15 T r5 ðtÞ dt þ b16 T r6 ðtÞ þ B2 ðtÞ ð13Þ 3.2.3. Heat balance equation for room number-3

dT r3 ¼ c11 T r1 ðtÞ þ c12 T r2 ðtÞ þ c13 T r3 ðtÞ þ c14 T r4 ðtÞ þ c15 T r5 ðtÞ dt þ c16 T r6 ðtÞ þ B3 ðtÞ

ð14Þ

3.2.4. Heat balance equation for dome shape room number-4 dT r4 ðM a C a Þ4 dt 9 8 8 8 8 P P P > > > > > > > m¼1½ðUAÞm ðT sol m  T r4 Þ þ a¼1½ðUAÞa ðT sol a  T r4 Þ þ b¼1½ðUAÞb ðT sol b  T r4 Þ > > > = < 8 8 ¼ P P > > þ ½ðUAÞ ðT  T Þ þ ½A s I þ ðUAÞ ðT  T Þ r4 r4 sol c k g Tk sol k > > c k > > > > c¼1 k¼1 > > ; : þ½ðUAÞ45 ðT r5  T r4 Þ  qa V a ca NðT r4  T a Þ

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A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

Fig. 3. (a) Schematic diagram of dome shaped roof made of eight walls and 24 roof surfaces. (b) Plan view of six interconnected rooms of passive house (all dimensions in ‘m’). (c) Typical wall and roof structure for RCC building in India.

or,

or,

dT r4 ¼ xðT r1 ; T r2 ; T r3 ; T r4 ; T r5 ; T r6 ; B4 ðtÞÞ dt

dT r4 ¼ d11 T r1 ðtÞ þ d12 T r2 ðtÞ þ d13 T r3 ðtÞ þ d14 T r4 ðtÞ dt þ d15 T r5 ðtÞ þ d16 T r6 ðtÞ þ B4 ðtÞ

ð15Þ

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A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

2









3

a11 T r1 ðtÞ þ K22 þ a12 T r2 ðtÞ þ L22 þ a13 T r3 ðtÞ þ M22 6 7   N2 O2 7 K3 ¼ h  6 4 þa14 T r4 ðtÞ þ 2 þ a15 T r5 ðtÞ þ 2 5  P2 þa16 T r6 ðtÞ þ 2 þ B1 ðtÞ

Similarly, the heat balance equations for the remaining dome shape roof rooms 5 and 6 can be expressed as follows: 3.2.5. Heat balance equation for room number-5

dT r5 ¼ e11 T r1 ðtÞ þ e12 T r2 ðtÞ þ e13 T r3 ðtÞ þ e14 T r4 ðtÞ þ e15 T r5 ðtÞ dt þ e16 T r6 ðtÞ þ B5 ðtÞ



2

ð26Þ 3

a11 ðT r1 ðtÞ þ K 3 Þ þ a12 ðT r2 ðtÞ þ L3 Þ þ a13 ðT r3 ðtÞ þ M 3 Þ

6 K 4 ¼ h  4 þa14 ðT r4 ðtÞ þ N3 Þ þ a15 ðT r5 ðtÞ þ O3 Þ

ð16Þ

7 5

þa16 ðT r6 ðtÞ þ P3 Þ þ B1 ðtÞ ð27Þ

3.2.6. Heat balance equation for room number-6

dT r6 ¼ f11 T r1 ðtÞ þ f12 T r2 ðtÞ þ f13 T r3 ðtÞ þ f14 T r4 ðtÞ þ f15 T r5 ðtÞ dt þ f16 T r6 ðtÞ þ B6 ðtÞ

Similarly, the expressions for the remaining fourth order Runge Kutta coefficients (e.g. L1, M1, N1, O1 and P1) can be expressed in the form of Eqs. (24)–(27).

ð17Þ

The solution of the above six first order linear differential Eqs. (12)–(17) can be obtained using the numerical technique known as fourth order Runge Kutta method explained by Chapra and Canale [19]. The values of initial room air temperatures of six rooms for each month of the year were obtained from the experimental results. Based on these initial values, the next hour room air temperatures of six interconnected rooms can be evaluated using fourth order Runge Kutta method as follows:

  K 1 þ 2K 2 þ 2K 3 þ K 4 h 6   L1 þ 2L2 þ 2L3 þ L4 T r2 ðt þ 1Þ ¼ T r2 ðtÞ þ h 6   M 1 þ 2M2 þ 2M 3 þ M 4 T r3 ðt þ 1Þ ¼ T r3 ðtÞ þ h 6   N1 þ 2N2 þ 2N 3 þ N4 T r4 ðt þ 1Þ ¼ T r4 ðtÞ þ h 6   O1 þ 2O2 þ 2O3 þ O4 h T r5 ðt þ 1Þ ¼ T r5 ðtÞ þ 6   P1 þ 2P2 þ 2P3 þ P4 T r6 ðt þ 1Þ ¼ T r6 ðtÞ þ h 6

T r1 ðt þ 1Þ ¼ T r1 ðtÞ þ

3.3. Input parameters of thermal model

ð20Þ

The design details, thermal properties of materials, overall heat transfer coefficient (U-value) and surface areas of different building components are mentioned in Table 1a–1d. These values are considered for simulating the thermal model of passive house. The representative day of the month is tabulated in Table 2 which is considered during simulation of program. The number of days in each month for four types of weather conditions (a–d) are tabulated in Table 3 as reported by Singh and Tiwari [20]. The number of days corresponding to each weather type is used to evaluate the monthly energy saving potential for the particular month. The latitude of the place was obtained from Table 4 as reported by Bansal and Minke [21] and Singh et al. [22] and for analysis of particular city location.

ð21Þ

3.4. Statistical error analysis for validation of thermal model

ð22Þ

In order to validate the results obtained theoretically with the experimental observations, statistical analysis presented by Chapra and Canale [19] is carried out to estimate correlation coefficient (rvalue) and root mean percentage error (e-value).

ð18Þ ð19Þ

ð23Þ

The expressions for the fourth order Runge Kutta coefficients used in Eq. (18) are given below:

K1 ¼ h 



a11 T r1 ðtÞ þ a12 T r2 ðtÞ þ a13 T r3 ðtÞ

3.4.1. Coefficient of correlation (r-value) When predicted values are validated with the experimental data, correlation between predicted and experimental values is presented with a coefficient known as coefficient of correlation. The coefficient of correlation can be evaluated by the use of following expression:



ð24Þ þa14 T r4 ðtÞ þ a15 T r5 ðtÞ þ a16 T r6 ðtÞ þ B1 ðtÞ 3    a11 T r1 ðtÞ þ K21 þ a12 T r2 ðtÞ þ L21 þ a13 T r3 ðtÞ þ M21 6 7   N1 O1 7 K2 ¼ h  6 4 þa14 T r4 ðtÞ þ 2 þ a15 T r5 ðtÞ þ 2 5  P1 þa16 T r6 ðtÞ þ 2 þ B1 ðtÞ 2

P P P n xi yi  ðxi Þ ðyi Þ q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P P P P n x2i  ð xi Þ2 n y2i  ð yi Þ2

ð25Þ

ð28Þ

Table 2 Representative day for each month of the year. Month

January

February

March

April

May

June

July

August

September

October

November

December

‘n’ day of the year

17

47

75

105

135

162

198

228

258

288

318

344

Table 3 Number of days for four weather types (a–d) in each month reported by Singh and Tiwari [20]. Weather type

January

February

March

April

May

June

July

August

September

October

November

December

a b c d Total days/month

3 8 11 9 31

3 4 12 9 28

5 6 12 8 31

4 7 14 5 30

4 9 12 6 31

3 4 14 9 30

2 3 10 16 31

2 3 7 19 31

7 3 10 10 30

5 10 13 3 31

6 10 12 2 30

3 7 13 8 31

1964

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

Table 4 Classification of climatic zones in India [21,22]. Climate type

Mean monthly ambient air temperature (°C)

Relative humidity (%)

Precipitation (rainfall) (mm)

Hot and dry Warm and humid Moderate Cold and Cloudy Cold and sunny Composite

>30 >30 25–30 <25 <25 This applies when six months

<55 <5 >20 >55 >5 <20 <75 <5 <20 >55 >5 <20 <55 <5 >20 or more do not fall under above category

The r value represents the coefficient of correlation between experimental (xi or Xexpt) and predicted (yi or Xpred) values from thermal model and n is the number of observations. If the value of the coefficient of correlation is greater than zero it means there is positive relationship between experimental observation and theoretical values and at its maximum value 1 means there is perfect relationship. Similarly for values less than zero there is negative relationship between experimental observations and theoretical values and when the value of correlation coefficient is zero it means there is no relationship. 3.4.2. Root mean square of percentage deviation (e-value) The predictions are made with the help of thermal modeling. The predicted values are validated with the experimental data. Here the closeness of predicted values and experimental data can be presented in terms of root mean square of percent deviation. The expression used for this purpose is as follows:

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 ðei Þ e¼ n where ei ¼

X predðiÞ X exp tðiÞ X predðiÞ

ð29Þ  100 and i = 1 to n (number of observations).

3.5. Energy saving potential of passive house

Number of clear days in a month

Example of city location in India (latitude, longitude, height above MSL) Jodhpur (26.3 °N, 73.02 °E, 224 m) Mumbai (19.12 °N, 72.82 °E, 14 m) Bangalore (12.97 °N, 77.58 °E, 920 m) Srinagar (34.08 °N, 74.83 °E, 1587 m) Leh (34.17 °N, 77.00 °E, 3300 m) New Delhi (28.58 °N, 77.20 °E, 216 m)

In the above Eqs. (30)–(32), the factors 1000 and 3600 are used for converting W into kW h. In Eq. (30), the term 20 °C represents the desired room air temperature throughout the year for the adobe house room. The maximum heating/cooling load of adobe house for the given climatic condition is defined using Eq. (31) which represents the amount of heating/cooling required for the ambient air to reach the desired room air temperature of 20 °C throughout the year. The positive value shows heating requirement and negative value show cooling requirement. This maximum or base case load is the reference load for the mud-house. Similarly, Eq. (32) represents the actual amount of heating/cooling requirement for the typical constructed adobe house. Hence, Eq. (30) represents the amount of heating/cooling energy savings for the mud-house. Similarly, the energy saving potential for remaining rooms can obtained using Eq. (30). If the value of Tr1 > Ta then it is heating energy saving potential and for Ta > Tr1 it is cooling energy saving potential of passive house. In case of New Delhi climatic condition, there is heating energy saving potential of passive house during the winter months November, December, January and February and for rest of the months of the year there is cooling energy saving potential of passive house. 3.6. Mitigation of CO2 emissions and carbon credits for the mud-house

The energy saving potential of passive house was determined on the basis of discussion with building simulation experts from recognized institutes and Energy and Buildings Journal experts. The methodology adopted for obtaining energy saving potential of passive house was reported and explained by Chel and Tiwari [15]. It was decided to estimate maximum or base case energy load of any building as benchmark to compare with passive building energy requirement. The energy saving potential of passive house is the difference between base case (or maximum) energy load of building and the actual energy load for passive building. The energy saving potential of each room of passive house (or mudhouse) was estimated from the following Eq. (30) based on the Chel and Tiwari [15]. The energy saving potential of mud-house room-1 (QSaving1) in kW h can be determined as follows:

Presently, there is special emphasis on embodied energy, mitigation of CO2 emissions and life cycle energy analysis of building. Hence, this paper has equally emphasized on the analysis of environmental impacts of the existing passive house by estimating embodied energy, energy payback time, CO2 emission mitigation potential and corresponding carbon credits as per Chel and Tiwari [15]. The CO2 emission mitigation (kg/year) due to the annual energy saving potential (kW h/year) from the existing passive house is estimated as follows:

CO2 emission mitigated ¼ 0:98  ð1 þ 0:4 þ 0:2Þ  Annual energy savingðkWh=yearÞ ð33Þ

Q Sav ing1 ¼ Q Base  Q Load ¼ ðM a C a Þ1  ðT r1  T a Þ 

1 ð3600sh

1

 1000Þ

ð30Þ

where,

Maximum or base case load; Q Base ¼ ðM a C a Þ1  ð20  T a Þ 

1 ð3600sh

1

 1000Þ

ð31Þ

The factor 0.98 kg of CO2 emission for producing 1 kW h energy from coal fired power plant in European countries was reported by Watt et al.[23] and Chel et al.[24]. In addition, the other factors are used particularly for India like 0.4 and 0.2 which accounts for the losses in transmission and distribution and loss of energy in inefficient appliances to meet the requirement respectively reported by Chel and Tiwari [15]. The amount of carbon credit earned by passive house can be calculated from Eq. (34).

Actual load of passive-house; Q Load ¼ ðM a C a Þ1  ð20  T r1 Þ 

1 ð3600sh

1

 1000Þ

ð32Þ

Carbon credit earned ð€Þ ¼ 10  Annual CO2 mitigation ðin metric tons=yearÞ

ð34Þ

1965

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

The factor considered in Eq. (34) is 10 Euro(€)/metric tons of CO2 mitigation in Asia (especially in India) from website [25] mentioned in the reference list. This factor represents the monetary value of one carbon credit earned due to mitigation of 1 metric ton of CO2 emissions. The embodied energy of the passive house was determined and given in Table 5c. The embodied energy analysis of reinforced cement concrete (RCC) building was presented by Gumaste [18]. The comparison was made between RCC structure and the passive house (mud-house) in Table 5c. The energy payback time was determined and mentioned in Table 5c. The detailed composition of RCC structure is shown in Fig. 3c and the wall and roof composition details are given below. RCC wall consists of external cement plaster (10 mm), concrete block (230 mm) and internal cement plaster (10 mm) while the RCC roof composed of roof tile (10 mm), cement mortar (40 mm), RCC slab (150 mm) and internal cement plaster (10 mm).

(b) Hazy day (fully)/type-b: if diffuse radiation is less than 50% or more than 25% of global radiation and sunshine hour is between 7 h and 9 h. (c) I Hazy and cloudy (partially)/type-c: if diffuse radiation is less than 75% or more than 50% of global radiation and sunshine hour is between 5 h and 7 h. (d) Cloudy day (fully)/type-d: if diffuse radiation is more than 75% of global radiation and sunshine hour is less than 5 h. Based on the hourly input climatic data for each four weather types (a–d) in each month as input, hourly values of six room air temperatures were determined from building simulation model for the given building details (from Table 1a–1d), latitude of the place and representative day of the month (from Table 2). The hourly and the daily values of energy saving potential of each room were evaluated to determine monthly data of energy saving potential based on daily energy saving values and number of days -corresponding to each weather type for particular month given in Table 3. The hourly observations were conducted on the typical clear day on January 18, 2008 for the global and diffuse solar radiation on horizontal surface and ambient air temperature as shown in Fig. 4a. Also, the hourly observations were recorded for passive house six room air temperatures as shown in Fig. 4b and c for New Delhi composite climatic condition. Based on the input climatic data of each location in India, the sol–air temperatures on all the outer surfaces of building components were calculated using Eq. (5). These calculated sol–air temperature values were used in heat balance equations of each room such as Eq. (12). The experimental observations of room air temperatures for the inverted U-shape rooms 1, 2 and 3 in New Delhi composite climate were shown in Fig. 4b. Similarly, the experimental observations for dome shape rooms 4, 5 and 6 were shown in Fig. 4c. The ther-

4. Input climatic data for building simulation and its experimental validation The input climatic data consists of global and diffuse solar radiation measured on hourly basis on the horizontal surface corresponding to four weather types (a–d) in each month, number of days which correspond to each weather type and monthly average hourly ambient air temperature data. All these climatic data for New Delhi, Jodhpur, Bangalore, Mumbai and Srinagar were obtained from Indian Meteorological Department, Pune. The four weather types (a–d) in each month were defined on the following basis: (a) Clear day (blue sky)/type-a: if diffuse radiation is less than or equal to 25% of global radiation and sunshine hour is more than or equal to 9 h.

Id

b

700 600

Ta

500 400

Ig

300 200

Id

100

20

Experiment on January 18, 2008

18 16

o

Ig

Air Temperature ( C)

Ta

2

17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

800

Experiment on January 18, 2008

Solar Radiation (W/m )

Air Temperature (oC)

a 19 18

14 12 10 8

T1 Tr1

6

T2 Tr2 T3 Tr3

4

TaTa

2 0

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7

Time (h)

0 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7

c

Room Air Temperature ( o C)

Time (h) 20 18 16 14 12 10 8 6 4 2 0

Experiment on January 18, 2008

T4 Tr4 T5 Tr5

Tr6 T6 TaTa

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7

Time (h) Fig. 4. (a) Hourly values of solar radiation and ambient air temperature in winter. (b) Experimental room air temperature for inverted U-shaped rooms (1, 2 and 3). (c) Experimental room air temperature for dome shaped rooms (4, 5 and 6).

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969 44

r = 0.97, e = 1.1 %

800

36 Tr3-Expt. Tr3-Pred

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7

Time (h)

32

700

r = 0.98, e = 0.3 % 600

28

500

24

r = 0.97, e = 2.1 %

20

Tr3-Model Tr5-Model Ta Tr3-Expt Tr5-Expt Ig Id

16 12 8 4

Air Temperature ( o C)

b

16.5 16 15.5 15 14.5 14 13.5 13 12.5 12 11.5 11 10.5 10 9.5 9 8.5 8

900

Experiment on June 15, 2007

40

400 300 200

Solar Radiation (W/m 2 )

15.0 14.5 14.0 13.5 13.0 12.5 12.0 11.5 11.0 10.5 10.0 9.5 9.0 8.5 8.0

Air Temperature ( o C)

a

Room air temperature (o C)

1966

100 0

0 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 r = 0.98, e = 0.2 %

Time (h) Tr5-Expt. Tr5-Model

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7

Time (h) Fig. 5. (a). Validation of the room-3 air temperature with respect to experimental results. (b) Validation of the room-5 air temperature with respect to experimental results.

mal model Eqs. (12)–(17) were solved using Runge Kutta fourth order method using MATLAB software for the given input climatic data from Fig. 4a, input passive house design parameters, thermal properties and weather types from Table 1a–1d, Table 2, Table 3, Table 4. The Table 4 shows the classification of climatic zones in India as reported by Bansal and Minke [21] and Singh et al. [22]. All the input parameters are substituted in Eqs. (12)–(17) to obtain hourly values of room air temperatures of six interconnected rooms of the passive house from Eqs. (18)–(23). Fig. 5a and b shows the hourly variations of predicted room-1 and room-5 air temperatures using the developed thermal model of passive house for the input weather condition of January for New Delhi (India). It is observed that the room air temperature values are temperate which was also reported by Eben [4]. The experimental results of the six interconnected rooms were compared with the thermal model results using statistical error analysis tool presented by Chopra and Canale [19]. The correlation coefficient (r-value) and percentage root mean square error (e-value) for room air temperatures of room 3 and 5 was estimated from Eqs. (28) and (29) and represented in Fig. 5a and b. Similarly, thermal model of passive house was found in good agreement with experimental results for the typical summer day (June 15, 2007) as shown in Fig. 6. The validation of simulation results using experimental results showed that the developed thermal model using quasi-steady state analysis is useful for predicting room air temperature of mud-house (or passive house). The temperate room air temperature for mud-house is because of the major construction material as ‘mud or adobe’ which has low thermal heat conductivity and high thermal heat capacity reported by Eben [4]. This thermal performance study for New Delhi climatic condition was completed for one year for four weather types in each month as defined by Indian Meteorological Department, Pune. The hourly values of maximum and actual heating/cooling loads for adobe house after renovation were determined using Eqs. (31) and (32). The summation of hourly values results in daily values corresponding to each weather types. These daily values were converted into monthly values using Table 3 reported by Singh and Tiwari [20] and as shown in Fig. 7. The summation of monthly values gives the annual heating/cooling

Fig. 6. Validation of room air temperatures using thermal model in June (summer).

load for the passive house as shown in Fig. 8. There is heating load during four winter months November, December, January and February in New Delhi. There is cooling load during rest of the months of the year in New Delhi. The maximum annual heating and cooling loads of adobe house (or passive house) for base case for the different Indian climatic conditions of New Delhi, Bangalore, Jodhpur, Mumbai and Srinagar were determined as shown in Fig. 8. Similarly, actual annual heating/cooling loads were obtained for the adobe house as shown in Fig. 8. The annual heating/cooling energy saving potential of the adobe house was determined as shown in Fig. 8. Based on the annual energy saving potential of the passive house, the annual mitigation of CO2 emissions and carbon credit earned by passive house were obtained using Eqs. (33) and (34). 5. Results and discussions These thermal properties of mud such as low thermal conductivity and high thermal heat capacity are responsible for attenuating and maintaining the inside room air temperature value nearly constant as compared to the wide range of fluctuations of ambient air temperature also reported by Eben [4]. The dome shape rooms have higher room air temperature by 1–2 °C as compared to inverted U-shape rooms because the sun exposed surface area of the dome shape rooms is larger as compared to inverted U-shape rooms. The experimental results showed that the passive house rooms have room air temperature within the range of 14–18 °C during winter months for ambient air temperature range of 6– 18 °C as shown in Fig. 4b and c. Also, during summer month in June, the room air temperatures are in the range of 24–28 °C for ambient air temperature range of 26–40 °C. During the harsh winter and summer ambient air temperatures was found in the range of 1–15 °C and 28–50 °C, respectively. The thermal comfortable room air temperature range in both winter and summer months were observed as 14–18 °C and 24–28 °C, respectively. The performance of mud-house was found satisfactory for the corresponding author of this paper in both winter and summer for above mentioned room air temperature range. Hence, this case study provides real insight of actual performance of mud-house in New Delhi climatic condition. Thermal comfortable room air temperature range for person living in Indian villages is also same as that of actual performance of mud-house. Hence, such mud-house home/shelters can easily provide naturally thermal comfortable zone for poor people residing in the desert (hot and dry climate) and remote mountains (cold and sunny climate) places. Few deaths have been recorded in India because of heat waves in summer and cold climate in winter. Hence, this naturally comfortable mud-house building is one of the solutions not only in India but also all over the world like Middle East countries for both rural and urban population.

1967

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

For base case:- Heating load = 2850 kWh/year; Cooling load = 5939 kWh/year, For Adobe house After renovation:- Heating load = 1370 kWh/year; Cooling load =4126 kWh/year

Heating/cooling load (kWh/month)

1500 1000

Load at base case

Load after renovation

500 0 -500 -1000 -1500

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Month of the year

Heating/cooling load (kWh/year)

Fig. 7. Monthly heating/cooling energy saving potential of the passive house in New Delhi.

New Delhi

12000 11000 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0

Heating load at base Cooling load at base case, HL (kWh/year) case, CL (kWh/year)

Banglore

Jodhpur

Heating load of adobe house, HLA (kWh/year)

Mumbai

Srinagar

Cooling load of Heating energy savingCooling energy saving adobe house, CLA = HL-HLA =CL-CLA (kWh/year) (kWh/year) (kWh/year)

New Delhi

2850.1

5939.1

1369.5

4126.2

1480.6

Banglore

1150.1

1641.3

583.9

1125.0

566.1

1812.9 516.2

Jodhpur

1489.9

5170.0

568.3

2773.9

921.6

2396.1

Mumbai

110.2

6018.0

81.4

3249.3

28.8

2768.7

Srinagar

11183.1

296.0

3903.7

140.1

7279.4

155.9

Fig. 8. Annual heating/cooling energy saving potential of the passive house in India.

Hence, the adobe house under study is promising solution for obtaining natural thermal comfort. The experimental results were used to validate the results of the thermal model which show the root mean square percentage error in the range of 0.1–3% as shown in Fig. 5a and Fig. 5b. The annual maximum heating/cooling loads at base case for New Delhi were determined as 2850 kW h/year and 5939 kW h/ year, respectively, as shown in Fig. 7. The actual annual heating/ cooling loads of adobe house were estimated as 1370 kW h/year and 4126 kW h/year, respectively. Hence, the annual heating/cooling energy saving potential of the passive house (or mud-house) is estimated as 1481 and 1813 kW h/year, respectively, from Fig. 8. The annual heating/cooling energy saving potentials for this passive house for different climatic zones or locations in India e.g. New Delhi, Bangalore, Jodhpur, Mumbai and Srinagar were determined and shown in Fig. 8. There is substantial energy saving potential of this passive house for both cold as well as hot climatic zones in India as shown in Fig. 8. Based on the annual energy saving potential of passive house, the amount of CO2 emission mitigation of the existing passive house was estimated as 5165 kg/year (or nearly 5.2 metric tons/ year) using Eq. (33). The annual carbon credits that can be earned by this renovated passive house building was estimated nearly € 52/year using Eq. (34). Hence, this passive house is an environment and eco-friendly option for residential buildings especially in semiurban and rural areas for providing year round thermal comfort conditions for rural population in India since they live without any air conditioner/air heater during summer and winter respectively. This passive house is an example of energy efficient building for construction of houses in rural areas of India. This will consid-

erably mitigate CO2 emissions from both building construction as well as due to energy saving potential of the house. Also, such passive house provides a habitat for rural population to sustain naturally the harsh winter and summer months. It is estimated that on an average adobe or mud-house can mitigate 5.2 metric tons/year CO2 emissions in to the atmosphere. The total numbers of households in Delhi state are 2,718,050 (nearly 3 million) as per the recent Census record accessed from website on February 19, 2008 [26]. If 5% of the total households in Delhi state are made from such adobe house design, then the annual CO2 emission mitigation into the atmosphere will be approximately 0.78 million metric tons which accounts for carbon credit of € 7.8 million/year. The total numbers of households in India are 137,747,384 (nearly 140 million) as per the recent Census record [26]. If 5% of the total households in India (i.e. 7 million households in India) are made of adobe houses in rural areas or semi-urban areas, then the annual CO2 emission mitigation into the atmosphere will be approximately 36.4 million metric tons/year which accounts for carbon credit of € 364 million/year. Hence, a substantial amount of CO2 emission can be mitigated annually in India from rural housing sector. The embodied energy analysis was carried out to estimate the amount of energy required for constructing the passive house using Table 5a, Table 5ba and b. The embodied energy of various building materials used for passive house are listed in Table 5a. The total embodied energy required for the passive house before and after renovation was estimated to be 187.8 GJ (or 1.99 GJ/ m2) and 216.1 GJ (or 2.3 GJ/m2), respectively for the given floor area of 94 m2 from Table 5b. The embodied energy per square meter of floor area for passive house is found lower as compared to

1968

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

Table 5a Embodied energy of building materials. Sr. no.

Material

Embodied energy

Amount of material used for mud-house

1 2 3 4 5 6 7 8 9

Paint Plywood Glass Cement Mild steel Brick Brick-tile Sand Mud

93.3 MJ/kg 10.4 MJ/kg 15.9 MJ/kg 4.2 MJ/kg 36 MJ/kg 5 MJ/brick 2.5 MJ/brick-tile 0.1 MJ/kg 0.0016 MJ/kg (soil is extracted from the location of construction site)

4.3 kg 1040 kg 98 kg 2074 kg 47.2 kg 32,952 numbers (120,235 kg) (40%) 10,712 numbers (18,761 kg) (6%) 4430 kg 160,992 kg (53%)

Table 5b Embodied energy analysis of passive house for six rooms. No.

Material used in mud-house

Embodied energy (MJ)

1 2 3 4 5 6 7 8 9 10

White wash for finishing of rooms and door painting Mud Sand used for flooring and filling material (or mortar) Glass used for Windows and Ventilators Glass used for daylighting of 4,5 and 6 rooms Burnt Brick used for main structure Steel used for one door and frames of ventilators Plywood used for doors and window frames Cement used in main structure Cement used for flooring (no tiles used for floor) Total embodied energy of old mud-house structure Brick-tiles used in renovation Cement used for filling mixture between brick-tiles Fine sea sand used for water proofing of roof top Additional embodied energy for renovation (EER) Total embodied energy (TEE) of renovated mud-house

400 260 443 1560 503 164,760 1700 10,816 3350 4020 187,812 MJ (or 47,593 kW h) 26,780 1340 154 28,274 MJ (or 7854 kW h) 216,086 MJ (or 60,024 kW h)

11 12 13 14 15

Table 5c Energy payback time (EPBT) and mitigation of CO2 emissions from mud-house. No.

Parameters for estimation

Results

1 2 3 4 5 6

Annual energy saving (AES) potential of renovated mud-house (both heating and cooling energy saving potential) Mitigation of CO2 emissions due to AES potential of renovated mud-house EPBT of renovated mud-house based on annual energy saving = (TEE/AES) Total floor area of mud-house six rooms Embodied energy of old mud-house (MJ/m2) Embodied energy of renovated mud-house (MJ/m2)

7 8

Total CO2 emissions from the construction of renovated mud-house of 94 m2 floor area Embodied energy of RCC building per unit floor area (MJ/m2) reported by Gumaste [18]

9 10 11 12

Total embodied energy of RCC structure house of floor area 94 m2 Total CO2 emissions from construction of RCC structure house of 94 m2 floor area (same as mud-house floor area) Mitigation of CO2 emissions due to construction of renovated mud-house compared to RCC structure house Carbon credits earned due to mitigation of CO2 emissions due to construction of mud-house instead of RCC structure building (assuming € 10/metric tons of CO2 mitigation)

3294 kW h/year 5165 kg/year 18 years 94 m2 1998 MJ/m2 2298.8 MJ/m2 (or 639 kW h/m2) 94 Tons 3702.3 MJ/m2 (or 1029 kW h/m2) 96,726 kW h 152 metric tons 58 metric tons € 580

3.7 GJ/m2 for RCC structure as reported by Gumaste [18]. Based on the embodied energy analysis of passive house, the energy pay back time of the passive house was estimated as 18 years from Table 5c. The total embodied energy required for renovated mudhouse was estimated as 60024 kW h from Table 5b. While the embodied energy for RCC house having floor area 94 m2 was calculated as 96726 kW h. Hence, the mitigation of CO2 emissions because of the construction of passive house as compared to RCC structure house is estimated nearly equal to 58 metric tons and corresponding carbon credit that can be earned by passive house estimated as € 580 as reported in Table 5c. 6. Conclusions The conclusions drawn based on the thermal performance results and embodied energy analysis of the passive house are as listed below:

 Room air temperature inside the mud-house building was found temperate in the range of 14–16 °C in winter (Ta = 1–15 °C) and 24–28 °C in summer (Ta = 28–50 °C) for New Delhi composite climatic condition.  The simulations results from the developed thermal model of the passive house were found in good agreement with the experimental observed data of room air temperature with correlation coefficient and root mean square percentage error in the range of 0.97–0.98 and 0.2–3%, respectively.  The annual heating/cooling energy saving potential of this passive house was estimated as 1481 and 1813 kW h/year respectively from Fig. 8.  The annual CO2 emissions mitigation is estimated as 5165 kg/ year (or nearly 5.2 metric tons/year). Hence, the total amount of CO2 emissions mitigation of the passive house for assumed 50 years of life time can be calculated as nearly 260 metric tons.

A. Chel, G.N. Tiwari / Applied Energy 86 (2009) 1956–1969

 The annual carbon credits for the passive house corresponding to the CO2 mitigation of 5.2 metric tons/year was estimated as € 52/year (assuming monetary value of one carbon credit € 20/ metric tons of CO2 mitigation). The total carbon credit earned by passive house for its assumed life time of 50 years was estimated as € 2600.  The annual heating/cooling energy saving potential of this passive house for different locations in India such as New Delhi, Bangalore, Jodhpur, Mumbai and Srinagar were determined as 3294, 1082, 3318, 2798 and 7435 kW h/year respectively from Fig. 8.  If 5% of the total households in India (i.e. 7 million households in India) are made of adobe houses in rural areas or semi-urban areas, then the annual CO2 emission mitigation into the atmosphere will be approximately 36.4 million metric tons/year which accounts for carbon credit of € 364 million/year. Hence, a substantial amount of CO2 emission can be mitigated annually in India from rural housing sector.  The embodied energy per unit floor area for mud-house and RCC structure house were determined as 639 kW h/m2 and 1029 kW h/m2 respectively. The annual energy output from mud-house was determined as 3294 kW h/year (Table 5c). Hence, the energy payback time for the renovated mud-house is nearly equal to 18 years from Table 5c.  If the mud-house construction is compared with RCC structure then the amount of CO2 emissions mitigated is nearly equals to 58 metric tons and corresponding carbon credit that can be earned € 580 from Table 5c.  The passive house (or mud-house) was found an energy efficient and eco-friendly habitat for achieving natural thermal comfort for rural population of India since they can not afford for window air conditioner or air heater in harsh summer and winter climatic conditions in India. Hence, the present passive house can also be called as ‘Zero energy green home’ or ‘Low carbon house’.  Among materials with comparable properties, one must choose not only the material with the lower embodied energy but also with the lower environmental impact. As far as construction practices are concerned, additional criteria should be considered like the lifetime of building materials, the compatibility of the lifetime among the layers, building materials and their maintenance needs over the building life cycle as suggested by Dimoudi and Tompa [16].

Acknowledgements We are thankful to the financial support provided by the Ministry of Human Resource and Development (Government of India). The authors are grateful for climatic data of selected Indian cities provided by the Indian Meteorological Department, Pune defining four weather types in each month. We thank encouragements from emeritus Prof. M.S. Sodha (IIT Delhi). We sincerely acknowledge Prof. B.V.V. Reddy for the valuable discussion on progress in stabilized mud blocks as building material in India during our meeting

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