Life Cycle Saving Analysis of an Earth-Coupled Building without and with Roof Evaporative Cooling for Energy Efficient Potato Storage Application

Abstract

Preservation of potatoes in a controlled cool environment (i.e., in cold storage) consumes a substantial amount of energy. The specific energy consumption in Indian cold storage has been estimated to be between 9 and 26 kWh/ton/year. In this article, the potential for minimizing the energy consumption in the refrigeration process of cold storage through passive cooling concepts (i.e., roof evaporative cooling and the earth integration of the storage building) was explored. These passive concepts of cooling have shown significant potential for lowering the cooling loads and the energy consumption in different types of buildings. Therefore, a feasibility analysis for a potato storage building, considering the effect of the passive cooling concepts, was conducted for three different climatic conditions (i.e., hot–dry, warm–humid, and composite) in India. The energy saving potentials in the cold storage were assessed by quantifying the thermal energy exchange between the indoor and outdoor environments using the modified admittance method. The effect of heat transfer through the building envelope on total energy consumption was estimated for the building having various sunken volumes (buried depths) without and with roof evaporative cooling. Further, the economic feasibility of adopting passive concepts was assessed in terms of life cycle saving compared to the base case. Results indicate that earth coupling without and with evaporative cooling has substantial potential to reduce the cooling load and can produce significant savings.

1. Introduction

Cold storage is used for preserving fruits, vegetables, meat, fish, and other perishable materials. The preservation process in cold storage consumes a massive amount of energy that is mainly dependent on the grid or other fossil-fuel driven power generators (e.g., diesel generator). The specific energy consumption in Indian cold storage has been measured to be between 9 and 26 kWh/ton/year [1]. Moreover, high post-harvest losses, especially in perishable products, due to unfavorable climatic conditions and increasing demand for fresh products throughout the year, have created a large demand for high-energy-consuming refrigerated cold storages [2]. The operation of such processes increases the risk of global warming and climate change. Furthermore, the increasing cost of fuels due to the limited availability of fossil fuel also increases the risk of businesses’ economic viability. Therefore, it is vital to enhance the energy performance of cold storage buildings in order to reduce the adverse climatic effect of fossil fuels and improve the economic viability of the cold storage industry. The energy-intensive process in cold storage can be moderated significantly by integrating natural/passive cooling strategies into the storage building [3]. The solar heat gain through the building envelope results in a larger refrigeration system and, eventually, higher energy consumption in cold storage. Generally, the storage temperature in a cold storage building is maintained at 4 °C or lower to keep perishable products (i.e., fruits, vegetables, milk products, meat and meat products, etc.) in fresh condition for longer periods. Despite the insulation of the building envelope, the direct exposure of the building surfaces to solar radiation and high ambient air temperatures lead to high heat gain. The results of a survey of cold storage buildings in India reveal that most of the cold storage facilities developed in the country are located above ground and exposed to direct sunlight. The direct sun exposure of the cold storage building not only has a negative impact on the overall energy consumption, but also increases the risk in the cold storage business. The use of the inherent cooling capacity of earth in the form of an earth-coupled building, and the considerable cooling potential of the roof evaporative cooling technique, have resulted in substantial energy savings in different buildings [4,5]. In some cases, the annual building energy consumption was reduced by 23–35% using only the earth-integrated building strategy [6]. Moreover, the energy reduction potential varies significantly with climate, thermo-physical condition of the location, type of earth surface, and the integrated volume of the building [7]. The reduction potential can also be improved by modifying the surrounding earth’s surface [8,9].

Furthermore, the roof in the building receives the greatest amount of solar radiation, which increases the cooling requirement, and thus needs to be addressed [10]. The heat gain through the roof has been estimated to represent over 50% of the total heat gain through all of the surfaces of the building. This excessive heat gain through the roof occurs because a horizontal roof experiences the highest solar radiation. Roof cooling, particularly using the evaporative cooling technique, is deployed to mitigate the undesirable excessive heat gain through the roof [11,12,13]. The roof evaporative cooling technique has been considered to be highly effective in lowering roof surface temperature and, eventually, reducing the heat gain through the roof.

Despite the substantial potential of earth coupling and roof evaporative cooling to minimize the energy consumption in conditioned buildings, it has hardly been explored for highly energy extensive cold storage buildings. Moreover, it is the authors’ understanding that the effect of varying the earth sunken depth, with and without roof evaporative cooling, on the economic feasibility of cold storage has not yet been explored in the literature.

Therefore, this study aimed to assess the techno-economic feasibility in terms of life cycle savings (LCSs) for cold storage buildings using the identified passive cooling concepts, i.e., earth integration and roof evaporative cooling, compared to above-ground buildings without the effect of any passive cooling concept. The use of such passive techniques individually produces considerable energy savings; however, a mix of two or more techniques may lead to much greater benefits. Therefore, the best combination of the passive techniques needs to be proposed to achieve the maximum energy savings. However, due to the large number of possible combinations of two or more passive concepts, it is a challenging task to identify the best suitable solution that requires the least investment and lowest operational cost, while reducing the adverse environmental effects. This study mainly focused on the assessment of the LCSs for different combinations of the two identified passive cooling strategies. For this purpose, thermal simulation was undertaken using a well-established and validated mathematical model, with the required modification based on the modified admittance method. The heat gain through the refrigerated building envelope and energy consumption were estimated using the model and validated against the energy consumption values measured from an existing cold storage building.

2. Description of the Storage Building

A single-room storage building, having a storage capacity of 2350 tons, was considered in this study. The dimensions of the cold storage room, which were previously optimized for minimum solar gain, were 24.1 m × 24.1 m × 12.0 m [13] (Figure 1), as also validated by the results of the field survey data [1]. The storage room was assumed to be optimally oriented at 31, 20, and 25 degrees with respect to true south in Indore, Jodhpur, and Chennai, respectively [14]. Openings (i.e., doors) are required in the building for loading/unloading and movements for maintenance and operational purposes. The thermal properties and operational frequency of the provided openings also play a role in overall heat transfer to the interior, and must be considered in the calculation procedure (as discussed in a Section 3.1.1). For calculation purposes and to make the calculation easier, a single door opening of 3 m² was assumed to be placed in the north wall. The purpose of locating the door in the north side is to protect the entrance from direct sun exposure. In the actual case, the number of door openings may vary on a case-to-case basis and doors may be located in any vertical surface. The construction characteristics of the building components were considered based on the prevailing practices used for cold storage building construction in India, and were verified in the survey. The graphical illustration of the roof, walls and floor used in this study is shown in Figure 2. The detail of the building construction characteristics identified during the survey were presented in our previous study [1]. The thermo-physical properties of the construction materials are listed in

Table 1. Construction materials’ thermo-physical properties.

	Unit	Plaster	R.C.C	Ground	Plaster	Insulation	Iron Net	Plaster
Specific heat	J/Kg-K	840	886	2235.0	840	1340	465	840
Thermal conductivity	W/m-K	0.72	1.58	0.50	0.72	0.035	53.60	0.72
Density	kg/m3	1762	2288	2460.0	1762	34	7833	1762

. The values of the heat transfer coefficients and other surface energy exchange parameters used in the calculation are given in

Table 2. Other relevant parameters used in the calculation.

Parameter	Unit	Value
ho—walls	W/m2-K	15.0
ho—roof	W/m2-K	18.0
ho (evaporative roof)	W/m2-K	100.0
hi—walls	W/m2-K	8.0
hi—roof	W/m2-K	6.0
hc (for dry-sunlit ground surface)	W/m2-K	22.7
ΔR for walls	-	0.0
ΔR for roof	-	61.1
ε	-	1.0
α (walls)	-	0.30
α (roof)	-	0.50

. The indoor temperature of the storage building was assumed to be 4 °C during the calculation, and was taken from the measurements from the existing cold storage building used for potato preservation [15].

3. Methodology

3.1. Thermal Energy Analysis

The heat gain analysis is divided into two main sub-sections, i.e., dealing with (Section 3.1.1.1) the heat gains due to solar energy receipt by the surfaces, and (Section 3.1.1.2) internal heat gains. Section 3.1.1 mainly explains the solar heat gains through the building envelope, without and with integrating the passive concepts, and Section 3.1.2 discusses the process of estimating the heat gains based on the internal loads and equipment.

3.1.1. Building Heat Gain

In this study, the cold storage building was considered in terms of three modes: (Section 3.1.1.1) a conventional on-ground building, (Section 3.1.1.2) an earth-coupled building with various sunken depths, and (Section 3.1.1.3) a building with roof evaporative cooling. The heat balance equations for the storage building in the individual modes were formulated and are presented separately in the sections below. The measured hourly average values of the ambient dry bulb temperature, humidity, wind speed, and direction, in addition to the solar global and diffuse radiations, were used for the calculation considering their effects on the periodic thermal response of the storage building [16].

3.1.1.1. On-Ground Cold Storage

In the conventional storage building (on-ground building), the walls and roof are directly exposed to the ambient environment and receive direct and diffuse radiations. Therefore, the heat balance equation for the on-ground conventional cold storage building is written as:

$$\begin{array}{ll}\hfill M_{rr}{C}_{rr}\frac{\partial T_{rr}\left(t\right)}{\partial t}=& {\displaystyle {\displaystyle \sum }_{m=1}^5}{A}_m{\displaystyle {\displaystyle \sum }_{n=-6}^{+6}}\frac{T_{m,n-}^s{T}_{R,o}}{Q_{m,n}^{\prime }}exp\left(in\omega t\right)+k_{g}F{L}^{\prime }\left({\overline{T}}_{gs}-T_{R,o}\right)+A_{gb}{\displaystyle {\displaystyle \sum }_{n=-6}^{+6}}\frac{-T_{R,o}}{Q_{m,n}^{\prime }}exp\left(in\omega t\right)+A_D{U}_D\left(T_a-T_R\right)\\ & +\frac{{\rho }_a{C}_a}{3600}N{V}_R\left(T_a-T_R\right)+Q_{R^{\prime }}+Q_M+Q_L+Q_{AUH}\end{array}$$

(1)

In the above equation, the first term on the right-hand side represents the cooling load from the walls and roof directly exposed to the sun. The next two terms (i.e., second and third) in the equation represent the steady state and periodic heat gains from the buried portion, respectively. The heat gains through the door and infiltration are represented by the fourth and fifth terms, respectively. Furthermore, the sixth, seventh, eighth, and ninth terms are used for the internal heat gains due to respiration of the storage commodity, occupancy, luminaries, and air-handling motors, respectively. The last four terms used in Equation (1) are also explained separately.

Q is a matrix element of the layer in the modified admittance method [16] and the element is determined as follows:

$$\left|\begin{array}{l}{P}^{\prime }{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{Q}^{\prime }\\ R^{\prime }{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{S}^{\prime }\end{array}\right|=\left|\begin{array}{l}1{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$h_o$}\right.\\ 0{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}1\end{array}\right|{\displaystyle \underset{}{\overset{}{}}}\left|\begin{array}{l}{a}_1{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{b}_1\\ c_1{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{a}_1\end{array}\right|{\displaystyle \underset{}{\overset{}{}}}\left|\begin{array}{l}{a}_2{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{b}_2\\ c_2{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{a}_2\end{array}\right|{\displaystyle \underset{}{\overset{}{-----}}}\left|\begin{array}{l}{a}_j{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{b}_j\\ c_j{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}{a}_j\end{array}\right|{\displaystyle \underset{}{\overset{}{}}}\left|\begin{array}{l}1{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$h_i$}\right.\\ 0{{\displaystyle }}_{}^{} {{\displaystyle }}_{}^{}1\end{array}\right|$$

where $a_j=\cosh ({\beta }_j{l}_j)$, $b_j=\sinh ({\beta }_j{l}_j)/(k_j{\beta }_j)$, $c_j=k_j{\beta }_j\sinh ({\beta }_j{l}_j)$, ${\beta }_j={(i\omega {\rho }_j{C}_j/k_j)}^{1/2}$. ${\rho }_j$, $C_j$, $k_j$, and $l_j$ are used to represent the density, specific heat, thermal conductivity, and thickness of the jth layer of the envelope component, respectively. $h_o$ and $h_i$ are the heat transfer coefficients for the outer and inner surfaces of the envelope components, respectively. As the climatic parameters (i.e., ambient temperature, solar radiation, and relative humidity) vary on an hourly and monthly basis, the heat transfer from the building surfaces varies in a similar pattern with a time lag due to the heat storage nature of the component. Furthermore, F is a shape factor and L’ is the characteristic length of the floor; the values of these parameters were taken from a previously published study [17].

$T_{m,n}^s$ and $\stackrel{-}{T_{gs}}$ are the sol-air temperature of the mth component of the envelope and the subsoil temperature, respectively. The subsoil temperature is assumed to be equal to the annual average sol-air temperature at the surface of the ground. The sol-air temperature and the average ground temperature are calculated using Equations (2) and (3), respectively [18]:

$$T_m^s=T_a+\left(\frac{\alpha \cdot I_m-\epsilon \Delta R}{h_o}\right)$$

(2)

and

$$T_{gs}=\frac{1}{h_{eff}}\left\{h_c\left[1+0.013R_1^{\prime }{\gamma }_h\right]T_a+\alpha I-\epsilon \Delta R-0.013R_2^{\prime }{h}_c^{}\left[1-{\gamma }_h\right]\right\}$$

(3)

where εΔR is the long wavelength radiation exchange between the sky and surface. $h_{eff}=h_c^{}\left[1+0.013R_1^{\prime }\right]$, $R_1^{\prime }$ and $R_2^{\prime }$ are constants for different conditions of earth surfaces (i.e., dry sunlit, dry shaded, wet sunlit, wet shaded, blackened sunlit, and blackened glazed sunlit). The values of constants for all surfaces were taken to be zero, with the exceptions of wet shaded and wet sunlit surfaces. The values of constants for the wet shaded and wet sunlit surfaces are listed in

Table 3. Values of $R_1^{\prime }$ and $R_2^{\prime }$ for different earth surfaces.

Surface	$R_1^{\prime }$	$R_2^{\prime }$
Wet sunlit	249	−3013
Wet shaded	249	−3013

[19]. It has to be noted that the present study was only performed for the dry sunlit surface.

The hourly average values of the climatic parameters used in the calculation were taken from the data book compiled by Mani and Rangarajan [19]. The annual mean ground temperatures for dry sunlit earth, for Jodhpur, Delhi, and Chennai, are 30.3, 28.1, and 31.2 °C, respectively [19].

3.1.1.2. Earth-Coupled Building

Earth integration protects the building from extreme weather conditions and reduces its solar thermal load. Therefore, the sunken depth of building can have a direct impact on energy consumption and the indoor environment [5]. Equation (1) can also be used for estimating the heat gain of a partially or fully earth-sunken storage building; the only adjustments required in the second term of Equation (1) are to the characteristic length and shape factor values, which depend on the dimensions of the buried portion of the building. In the present case, both vary with the sunken depths taken from ref. [17].

3.1.1.3. The Building with Roof Evaporative Cooling

Roof evaporative cooling has considerable potential for reducing the cooling loads in different buildings [20,21]. Evaporative cooling can be achieved by creating a pond on the roof [22], using a thin film of water [23], or running water over the roof surface [24]. In this study, a film of water was assumed to be placed on the flat roof due to its easier operation and lower maintenance requirement [25]. In this approach, a water film was assumed to be created by spreading gunny bags over the roof and water is assumed to be sprayed continuously/intermittently using a water sprayer to keep the gunny bags wet during sunshine hours. The heat balance equation (Equation (1)), for a cold storage building with roof evaporative cooling, can be modified as:

$$\begin{array}{ll}\hfill M_{rr}{C}_{rr}\frac{\partial T_{rr}\left(t\right)}{\partial t}=& {\displaystyle {\displaystyle \sum }_{m=1}^4}{A}_m{\displaystyle {\displaystyle \sum }_{n=-6}^{+6}}\frac{T_{m,n}^s-T_{R,0}}{Q_{m,n}^{\prime }}\exp \left(in\omega t\right)+A_r{\displaystyle {\displaystyle \sum }_{n=-6}^{+6}}\frac{\left(T_{wr,n}-T_{R,0}\right)}{Q_{r,n}^{\prime }}\exp \left(in\omega t\right)+k_{g}F{L}^{\prime }\left({\overline{T}}_{gs}-T_{R,0}\right)\\ & +A_{gb}{\displaystyle {\displaystyle \sum }_{n=-6}^{+6}}\frac{-T_{R,o}}{Q_{m,n}^{\prime }}\exp \left(in\omega t\right)+A_D{U}_D\left(T_a-T_R\right)+\frac{{\rho }_a{C}_a}{3600}N{V}_R\left(T_a-T_R\right)+Q_R+Q_M+Q_L+Q_{AHU}\end{array}$$

(4)

In this equation, the second term represents the heat gain through the evaporatively cooled roof. All other terms on the R.H.S. of the equation have the same meaning as in Equation (1). The Q’ values in the denominator of the first, second, and fourth terms on the R.H.S. of Equation (4) are matrix elements and calculated as per the procedure explained in Section 3.1.1.1.

Tw represents the temperature of the water film and can be estimated by taking the indoor temperature and other relevant parameters into account. The following heat balance equation is used to estimate the water film temperature [16]:

$$M_w{C}_w\frac{\partial T_w}{\partial t}=U_w\left(T_{r,n}^S-T_w\right)+{\displaystyle {\displaystyle \sum _{n=-6}^{=+6}}\frac{P_n^r{T}_{wn}-T_R}{S_{r,n}}}\exp (in\omega t)$$

(5)

where $T_r^s=\frac{{\alpha }_{\omega }{I}^r+h_1{T}_a-(1-{\gamma }_h)R_0{R}_2}{h_r+h_c+R_0{R}_1}$, $h_1=h_r+h_c+{\gamma }_h{R}_0{R}_1$, $R_0=0.013h_c$, $R_1=168.1$, $R_2=-969.1$.

For estimation purposes, M_w = 0 was considered by assuming the water layer over the roof surface is extremely thin. Furthermore, S_r,n in the denominator of Equation (5) is again a matrix element of the modified admittance method and calculated as per the procedure explained previously in this section.

3.1.2. Internal Heat Gain

3.1.2.1. Respiration Load

Fresh fruits and vegetables release heat and CO₂ even after harvesting because they are still alive. Because of their perishable nature, most fruits and vegetables need to be stored in a controlled environment to retain their flavor and quality for an extended period. The amount of heat liberated by these perishable commodities in a controlled environment is directly proportional to the storage temperature in most cases, and increases with the temperature. However, the optimum storage temperature for potato is 4 °C; at this temperature, potato liberates minimal heat, of around 0.019 kW/tonne [15]. In this study, only the optimum storage temperature was used.

3.1.2.2. Occupancy Load

Occupants located inside the storage building for loading/unloading and other purposes also contribute to the overall heat load of the refrigeration process of the building. The heat liberated by personnel occupancy in the storage building can be estimated as follows [26]:

$$Q_M=\left(272-6t_R\right)W$$

(6)

where Q_M = the heat liberated per person (W), t_R = the storage temperature of the refrigerated space (K).

3.1.3. Heat from Luminaries

Luminaries installed in the cold storage building also liberate heat and increase the building’s cooling requirement. The luminaries convert electrical energy mainly into light, which eventually turns into heat after multiple reflections, and a small fraction of electricity is also directly converted into heat. The total heat released by a luminary can be calculated by the following relation [27]:

$$Q_L={{\displaystyle }}_{}^{}{R}_l\times f$$

(7)

where R_l is the rated power of the luminary and f is the heat-releasing factor per watt of the luminary, which depends on the luminary type (f = 1 for incandescent bulbs and f = 1.25 for conventional fluorescent tubes) [27].

3.1.4. Heat Gain from AHU’s Motors

A fraction of the input power to the motor/fan is promptly converted into heat as a result of inefficiency of the motor/fan, thus increasing the cooling load in the storage. The total input electrical energy in the motor/fan finally changes into heat and leads to an increase in overall energy consumption in the storage building. The heat gain from the motor/fan due to the inefficiency factor is estimated by [17]:

$$Q_{AHU}=\frac{P_R}{{\eta }_{FM}}$$

(8)

where P_R is the rated power (watts) of the motor and ${\eta }_{FM}$ is the efficiency of the motor. Here, the value of efficiency was taken as 0.85.

As per the prevailing cold storage practices, the input values of personnel occupancy, luminaries, and fans in the AHU were considered in the calculation to be 4.11 (54 watts each) and 100 (70 watts each), respectively [17].

3.2. Estimation of Energy Consumption

Annual electrical energy consumption in the cold storage building is the sum of the energy consumed by the refrigeration unit and the water pump that is used for transporting the water to the roof for evaporative cooling. It is assumed that the energy consumption for other purposes such as lighting is very small (roughly 1–2%), and was not considered in this analysis because it is invariant during the complete analysis and does not affect the results. Therefore, the total annual energy consumption in the cold storage building for cooling application can be calculated by:

$$E_a={\displaystyle {\displaystyle \sum _{mn=1}^{12}}N{D}_{mn}\times }\left({\displaystyle {\displaystyle \sum _{hr=1}^{24}}(P_{RU}+P_{wp}}{)}_{hr}\right)$$

(9)

where P_RU and P_w are the electrical energy required for running the refrigeration unit and water pump [24], respectively, and can be expressed as:

$$P_{RU}=\frac{Q_T}{COP}$$

(10)

$$P_w=\frac{5.796x{10}^{-4}\times \stackrel{.}{m_w\times H}}{\eta }$$

(11)

In the absence of evaporative cooling, P_w = 0. H is the required head for water pumping. The value of the water pump efficiency (η) was taken as 0.75. ${\stackrel{.}{m}}_w$ is the water evaporation rate during roof evaporative cooling, and can be estimated using the following equation [24]:

$$\stackrel{.}{{\stackrel{.}{m}}_w=A_r\frac{0.013h_c\left[R_1{T}_w+R_0-{\gamma }_h{R}_1{T}_a-{\gamma }_h{R}_2\right]}{L_w}}$$

(12)

3.3. Life Cycle Saving Analysis

Furthermore, the economic feasibility of the adopted passive cooling concepts (i.e., earth integration and roof evaporative cooling) was assessed in terms of life cycle saving (LCS) [28,29,30,31,32,33,34]. The LCS was estimated for each considered alternative compared to the conventional refrigerated storage building. This analysis helps to estimate the overall cost of the alternative strategies that were evaluated in the present study. The life cycle saving analysis ensures that the optimized option will provide the lowest overall cost of ownership and is consistent with its functioning [35,36,37,38]. The LCS is the difference between the life cycle costs (LCCs) for the base case traditional cold storage (on-ground) and the proposed alternative storage building considering the effect of passive cooling strategies. The life cycle cost (LCC) for conventional/earth-coupled cold storage includes the capital costs of the building and refrigeration unit, and the cost of operation and maintenance; whereas the LCC for the cold storage building with roof evaporative cooling includes the capital costs for the building, refrigeration unit, and water pump, and the costs of operation and maintenance. The analysis also involves some basic assumptions that are listed in

Table 4. Input values used in the life cycle saving analysis.

Parameters	Value *
Cost of plastering (INR/m2)	50.00
Cost of RCC construction (INR/m3)	4000.00
Cost of Insulation (INR/m3)	2100.00
Cost of water pump (INR)	15,000.00
Cost of refrigeration unit (INR/TR)	18,000.00
Electricity charge (INR/kWh)	4.50
Interest rate	11.0%
Rate of inflation	6.80%

* Rates are in Indian Rupees (INR) as per the market survey.

.

In the calculation, the lives of the building envelope, refrigeration unit, and setup used for the evaporative cooling were considered to be 75, 20, and 10 years, respectively. These values were reasonably selected based on the literature.

3.4. Description of Climatic Conditions for Case Study Locations

Variations in the climatic conditions and climate types are observed at different locations within India. The country is primarily divided into five different climatic zones based on the variation in the climatic parameters (i.e., temperature, radiation, and humidity) [29]. The climate types into which the country has been categorized are: (i) hot–dry; (ii) hot and humid; (iii) composite; (iv) temperate; and (v) cold. The first three climates mainly require cooling and were hence considered for the analysis in this study. Jodhpur, Chennai (Madras), and Delhi are representative cities of the hot–dry, hot–humid, and composite climates; therefore, the weather data for these locations used in the calculations were taken from [39]. The monthly average values of the weather parameters are given in

Table 5. Monthly mean values of used meteorological data for Jodhpur, Chennai, and Delhi climates.

Month	Jodhpur				Chennai				Delhi
	ta	SG	SD	γh	ta	SG	SD	γh	ta	SG	SD	γh
1	17.12	195.21	48.29	37.71	24.60	218.17	77.54	76.67	13.58	164.46	52.33	66.25
2	20.64	231.13	57.08	28.67	25.63	262.13	71.63	74.92	17.62	206.21	62.13	53.08
3	26.29	270.58	74.46	23.83	27.73	286.58	76.96	73.67	22.67	254.29	78.88	46.17
4	30.95	298.50	98.46	22.21	29.77	287.71	88.58	74.13	29.00	288.58	103.21	33.54
5	34.49	313.04	113.00	26.08	31.56	272.04	106.71	66.83	32.66	303.08	122.88	31.92
6	34.07	292.29	131.08	45.79	30.88	243.04	119.29	63.29	34.17	269.58	146.71	42.21
7	31.06	247.54	141.71	66.33	29.62	225.29	129.67	67.79	31.02	223.75	130.33	69.33
8	29.80	229.83	132.79	71.04	28.86	232.42	125.67	73.71	29.25	209.92	113.75	79.25
9	29.06	253.00	82.96	68.42	28.70	238.21	107.50	76.50	28.54	231.17	90.25	71.88
10	27.92	242.67	52.13	39.08	27.50	201.88	93.92	81.25	25.68	221.83	61.42	59.33
11	22.58	202.88	39.50	34.00	25.54	180.83	85.75	82.29	19.80	187.04	47.46	57.29
12	18.46	183.38	39.29	40.04	24.61	177.29	84.67	79.88	14.48	159.38	46.92	65.88

for representative purposes only. However, the hourly values were used in the calculation of heat gain for the different alternatives considered in this study. The values of the weather data were taken from a reliable source [39], which was documented based one many years of average values from the measurements using calibrated equipment at the sites in the three selected cities.

4. Validation of the Model

The accuracy of the predicted results for the heat transfer from different components of the model used in this study was verified previously by various researchers [8,9,12,17,23], and thus can be trusted. However, to ensure the reliability of the estimated energy consumption values for the cold storage building using the model in the present study, the actual monthly energy consumption data from the existing cold storage building were used. One of the cold storage buildings was simulated by giving input values of the building construction characteristics, product storage pattern, internal storage temperature, and other parameters, which were measured during the survey [1]. The details of the measured values can be seen in refs. [1,17]. Furthermore, the monthly energy consumption values were collected from the storage building for three years and were calculated using the developed model. Figure 3 shows the comparative values of the simulated and actual measured energy consumption. The pattern indicates the predicted results are in an acceptable range. However, there may be some uncertainties and unpredictability in the predicted values due to various reasons. For example, we used a fixed stable indoor storage temperature, but in the actual case there may be variability in the storage temperature. Furthermore, a fixed infiltration rate was used in the simulation; however, the infiltration may vary from the fixed value. Therefore, the uncertainty and unpredictability were estimated in terms of statistical parameters such as the mean bias error (MBE), root mean square error (RMSE), and percent root mean square error (PRMSE), as follows:

$$MBE=\frac{1}{n}{{\displaystyle \sum }}_{k=1}^n\left(m-p\right)$$

(13)

$$RMSE=\sqrt{\frac{1}{n}{{\displaystyle \sum }}_{k=1}^n{\left(m-p\right)}^2}$$

(14)

$$PRMSE=\left[\sqrt{\frac{1}{n}{{\displaystyle \sum }}_{k=1}^n{\left(\frac{m-p}{m}\right)}^2}\right]\times 100$$

(15)

where n is number of simulated values for each year, and m and p represent actual measured and predicted values, respectively. The estimated values of MBE, RMSE, and PRMSE for the energy consumption per ton are listed in

Table 6. Performance of predicted model.

	MBE	RMSE	PRSME (%)
Year-I	0.03	1.85	15.69
Year-II	0.01	1.00	12.43
Year-III	0.07	1.08	13.57

.

The variation in the values for the energy consumption can be attributed to the effect of variation indoor temperature, rate of infiltration, and any other deviation in the actual operation of the cold storage building.

5. Results and Discussion

Peak and annual values of cooling loads for the storage building were estimated and compared for all of the selected alternatives. The impact of the sunken volume of the earth-coupled building and roof evaporative cooling on the cooling loads was assessed in the cold storage building for various sunken volumes of the building, without roof evaporative cooling (WOEC) and with roof evaporative cooling (WEC). Figure 4a,b shows the percentage reduction in peak and annual cooling loads, without and with roof evaporative cooling for three Indian locations (i.e., Jodhpur, Delhi, and Chennai) in three different climates, i.e., hot and dry, composite, and warm–humid, respectively. The figures clearly indicate that there is no significant reduction in cooling loads for a small sunken volume (up to 20% sunken). The cooling loads reduce significantly and linearly with a sunken volume higher than the 20% in both cases, i.e., WEC and WOEC. Results also indicate that the integration of the roof evaporative cooling alone substantially reduces the cooling requirement in the storage building. The effect of roof evaporative cooling alone can be seen at the origin in the above figures, which indicates that the building is completely on-ground.

The roof evaporative cooling is comparatively less effective in the warm–humid climate than in the hot–dry and composite climates. The reason for the lower effectiveness of the technique is the reasonably lower water evaporation because of high humidity. Similarly, the evaporative cooling option reduces the cooling loads more effectively in hot–dry and composite climates due to the higher evaporation of the water. The evaporation rates of water with climatic parameters for the three considered locations are presented in Figure 5, Figure 6 and Figure 7, respectively.

The reduction potentials in the peak cooling load in hot–dry, composite, and warm–humid climates were estimated to be approximately 4%, 3.5%, and 2% respectively (Figure 4a). Moreover, the LCSs for varying sunken volumes of the storage building without and with roof evaporative cooling for the three climates are shown in Figure 8, Figure 9 and Figure 10.

Figure 8, Figure 9 and Figure 10 clearly indicate that earth coupling of the building WOEC was not found to be effective for a sunken volume up to 15% in any of the climates; however, the LCS of the earth-integrated building increases linearly for sunken volumes higher than 15%. Similarly, earth coupling of the building WEC was found to be an effective option only if the sunken volume of the building is at least 35%. In other words, the positive values of LCS can be achieved by considering the earth-coupled building options WOEC and WEC only if the buildings are sunken by at least 25% and 35%, respectively. Moreover, LCS increases with sunken volume in both cases i.e., WOEC and WEC. However, the WEC option becomes effective in a hot–dry climate for sunken volumes greater than 50%. In the other two climates, the effective value of the minimum sunken volume for positive LCS is 65%. This variation occurs because of the decrease in the operational cost of the water pump with an increase in the sunken volume. Equation (11) clearly indicates that the energy consumption in the water pump is almost directly proportional to the head. The required head in the evaporative cooling decreases as the sunken volume increases and, eventually, energy consumption of the water pump decreases. The maximum savings that can be achieved through earth coupling of the building, WOEC and WEC, were estimated to be 9–11% in a hot–dry climate, and 9–10% in both warm–humid and composite climates.

6. Conclusions

Cold storage is one of the key elements in the cold chain infrastructure in India. The limited refrigerated storage facilities and their energy-extensive operation create continual challenges for minimizing post-harvest losses, particularly of perishable products. Moreover, due to the limited availability of fossil fuels, increasing fuel costs, and increasing temperatures, the operation of cold storage, and hence the refrigerated products, are expensive. Different passive cooling options have significant potential to reduce the heating load, the energy consumption in the refrigeration process, and, eventually, the operation costs, and thus improve the financial viability of cold storage facilities. In this study, the effect of earth integration and roof evaporative cooling on the energy consumption and life cycle savings in cold storage were assessed. Simulations were performed in three different climatic conditions in India. From the discussions of the simulation results, the following conclusions can be drawn:

• The cooling loads in a cold storage building can be reduced significantly by considering the earth coupling of the building and the roof evaporative cooling, either individually or in mixed mode.
• The energy saving potential increases with increasing the sunken depth; however, the magnitude of the saving potential is dependent on the climate.
• The overall energy consumption in the cold storage can be reduced by 4%, 3.5%, and 2% in hot–dry, composite, and warm–humid climates, respectively, by using only the roof evaporative cooling.
• In terms of LCS, the earth integration of the building without roof evaporative cooling was found to be effective only if the sunken volume is at least 25%; however, earth integration with roof evaporative cooling was found to be effective if the building is buried by at least 35%.
• The minimum effective sunken volumes of the earth-coupled building, with and without roof evaporative cooling, are 65% for the warm–humid climate and 50% for both the composite and hot–dry climates.
• The maximum LCSs achieved in the completely sunken building, WOEC and WEC, were estimated to be approximately 9–11% in both the hot–dry climate, and 9–10% in the warm–humid and composite climates.

The outcome of this study reveals that the integration of passive cooling strategies in cold storage buildings not only increases the economic feasibility of the cold storage operation, but also reduces the adverse climate change impact caused by the burning of fossil fuels. Furthermore, it will help in minimizing the post-harvest losses of perishable products, and increase the sustainability of the cold chain infrastructure and overall food security.