01_5-9-2

Available online 13 May 2011

hot storage tanks, an auxiliary heater as well as two cold storage

rmt en

Mots cles : accumulation thermique ; syste`me a` absorption ; reseau neuronal

Solar air conditioning is an emerging market with a huge

growth potential. Peak cooling demand in summer is associ-

ated with high availability of solar radiation, which offers an

excellent opportunity to exploit solar energy with heat-driven

cial technologies are available on a limited basis. However,

a strong attention is put on research on other applications

including photovoltaic-operated refrigeration cycles and solar

mechanical refrigeration (Balaras et al., 2006). Recently

research has been devoted to improving the main

* Corresponding author. Tel.: 34 950 015914; fax: 34 950 015477.

ava i lab le at www.sc iencedi rec t .com

: w

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 4E-mail address: [email protected] (F.J. Batlles).1. Introduction cooling machines. For low power cooling systems, commer-de bacs daccumulation employant des reseaux neuronauxartificielsKeywords:

Thermal storage

Absorption system

Neural network

Etude sur la perfodair fonctionnan0140-7007/$ e see front matter 2011 Elsevdoi:10.1016/j.ijrefrig.2011.05.003out the study about the necessity of the integration of hot water storage tanks to solar

system. Subsequently, the unique Artificial Neural Network (ANN) model with the lowest

number of input variables has been proposed with the main purpose to predict the coef-

ficient of performance and the cooling capacity of the absorption chiller. The configuration

5-9-2 (5 inputs, 9 hidden and 2 output neurons) was found to be the optimal topology. The

results demonstrate proper ANNs predictions with a Root Mean Square Error (RMSE) of less

than 0.70% and practically null deviation, which can be considered very satisfactory.

2011 Elsevier Ltd and IIR. All rights reserved.

ance dun syste`me de conditionnementpartie grace a` lenergie solaire et muniAccepted 2 May 2011 tanks. The hot storage tank circuit was further investigated. In first step, we have carriedReceived in revised form

29 March 2011

system consists main

a cooling tower, twoPerformance study of solar-assisted air-conditioning systemprovided with storage tanks using artificial neural networks

S. Rosiek, F.J. Batlles*

Dpto. Fsica Aplicada, Universidad de Almera, Ctra. Sacramento s/n, La Canada de San Urbano, 04120 Almera, Spain

a r t i c l e i n f o

Article history:

Received 3 December 2009

a b s t r a c t

This study presents the performance of solar-assisted air-conditioning system provided

with two storage tanks installed in the Solar Energy Research Center (CIESOL) building. The

ly of solar collectors array, a hot-water driven absorption chiller,www. i ifi i r .org

journa l homepageier Ltd and IIR. All rightsww.e lsev ier . com/ loca te / i j re f r igreserved.

T2 second storage tanks average temperature [C]

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 4 1447components of the solar absorption cooling system, such as

the solar collector, the absorption chiller, and hot water

storage tank.

The main purpose of storage in a solar-assisted air-condi-

tioning system is to overcome mismatches between solar

gains and cooling loads. The most common application is the

integration of a hot water buffer tank in the heating cycle of

the thermally driven cooling equipment. The excess solar heat

can be stored in the heat storage unit and is available if the

solar heat is not sufficient and it serves as a buffer reservoir to

have nearly constant heat input. Kreider and Kreith (1981)

have reported that the system can be operated more

efficiently by using two hot storage units as a heat reservoir,

set at two different temperature ranges (Li and Sumathy,

2000, 2001; Henning, 2004).

Artificial Neural Networks have been increasingly used in

recent years to predict or to improve nonlinear system

performance in HVAC&R (S encan et al., 2006; Wong et al.,

2010). The ability to learn is one of the outstanding charac-

teristics on an ANN. ANNs can model multiple parameters

simultaneously for nonlinear systems and are now widely

used for predictive control, such as solar radiation (Lopez

et al., 2001, 2005; Bosch et al., 2008), energy use prediction,

energy optimization (Chow et al., 2002), data trending, and

Nomenclature

Cp specific heat capacity of water [4.18 Kj kg1 K1]

I incident radiation intensity [Wm2]Tamb ambient air temperature [C]Tout leaving flat-plate collectors temperature [C]mc collectors mass flow rate [m

3 h1]mg generators mass flow rate [m

3 h1]Teg entering generators temperature [C]Tlg leaving generators temperature [C]me evaporators mass flow rate [m

3 h1]

Tee entering evaporators temperature [C]Tle leaving evaporators temperature [C]mac absorbers and condensers mass flow rate

[m3 h1]optimum start and stop (Wang, 2001; Chang, 2007).

There had been several studies on the performance

prediction of the absorption chiller system (Lazzarin et al.,

1993; Martnez and Pinazo, 2002; Syed et al., 2005; Asdrubali

and Grignaffini, 2005; Helm et al., 2009), however very little

work has been conducted for the total solar cooling systems

based on absorption chiller and provided with hot water

storage tanks. Hence the main purpose of this study was to

analyze the essential importance of the hot water tank inte-

gration and to determine ANN model able to estimate the

coefficient of performance and cooling capacity for usage in

control system. The final goal of the solar-assisted air-condi-

tioning system is to operate with stable and high values of the

coefficient of performance, and to maximize the use of solar

thermal energy. To meet this objective we need to know in

which conditions our systems works more efficiently, at the

same time covering building necessity. Until now the control

system has been utilized only by determining the information

about temperatures found in the collectors field, storage tanksand the load fraction in the building with any consideration

about the systems optimal operation points. This is the

fundamental difference between systems with and without

application of ANN models. On the other hand we intend to

estimate the cooling capacity in order to operate with cold

water storage tanks. Using this kind of tanks we are able to

store the excess cooling capacity of the chiller and use it when

thecoolingproductiondoesnot cover thebuilding load (clouds

alternation or mismatches between solar gains and building

loads). This system could permit the absorption chiller to be

operated even when there is no demand, increasing the use of

solarenergy,preventing thesuddenstart/stop (on/off cycles) of

the chiller due to low cooling demand and allowing to realize

the earlier chiller start-up, so we need to predict values of the

cooling capacity to choose the best way of system control. In

this way we maximize the use of solar thermal energy, avoid-

ing the CO2 emissions to the atmosphere. In this work we

determined a methodology, which could be easily adapted to

other systems with the main goal to improve the design of

solar-assisted systems bearing in mind number of measure-

ments and operation points as well the maximisation of the

solar thermal energy. Our study allows also reducing the

monitoring cost, since we are able to avoid a lot of redundant

measurementspoints through the selectionof theANNs input

S2 temperature in the upper part of the second

storage tank [C]COP coefficient of performance

Qcool cooling capacity [kW]

Qev the evaporator load [kW]

Qgen the heat delivered to generator [kW]Teac entering absorbers and condensers temperature

[C]Tlac leaving absorbers and condensers temperature

[C]T1 first storage tanks average temperature [C]parameters. In the first phase of the present work we present

the behaviour of the system without hot water storage tanks.

To achieve this goal the study about the solar system with no

hot water storage tanks integration was presented. Secondly,

the solar-driven hot water storage tanks applied to above

mentionedsystemwere investigatedmoreprofoundly. Finally,

we determine an artificial neural networkwith themain scope

to predict the coefficient of performance and the cooling

capacity of the absorption chiller fed by water provided only

from hot water storage tanks.

2. Description of solar-assisted air-conditioning system

In this study, we use data registered in the solar-assisted air-

conditioning system installed in the Ciesol building situated

on the Campus of the University of Almeria. The system

employs a flat-plate solar collectors array with a total surface

of 160 m2, the hot-water driven single-effect LiBr-H2O

absorption chiller with a rated capacity of 70 kW (Yazaki), the

cooling tower, two hot storage tanks with a capacity of 5000 l

each, an auxiliary heater and two cold storage tanks. Analysis

of the aforementioned system and its various operation

modes has been recently presented by Rosiek and Batlles

(2009). Fig. 1 presents the view of the CIESOL building with

the flat-plate solar collectors array installed on the roof and

the main system components.

In this study, we analyze the behaviour of this system

supplied with only hot water storage tanks to satisfy the cool-

ing demand of the Ciesol building. Fig. 2 presents the general

scheme of this system operating in solar cooling mode. In the

present paper, we use measurements of global radiation,

temperatures and mass flow rates of the absorption chiller

and storage tanks acquired with a 1 min sampling period

with the main goal to analyze the behaviour of the system

and to predict coefficient of performance (COP) and cooling

capacity Qcool of the absorption chiller.

The coefficient of performance (COP) is obtained from the

following equation:

COP Qev=Q

gen m

e Cp Tee Tle=m

g Cp

Teg Tlg

(1)

where Qev is the evaporator load Q

cool,Q

gen is the heat deliv-

ered to generator, me is the evaporators mass flow rate, Cp is

the specific heat capacity of water, Tee is the entering

evaporators temperature, Tle, is the leaving evaporators

temperature, mg is the generators mass flow, Teg is the

entering generators and Tlg is the leaving generators

temperature.

3. Integration of the hot water storage tanks

In the following subsections we want to emphasize the

essential importance of thehotwater tank integration to solar-

assisted air-conditioning system. To achieve this goal the

study about the solar-assisted air-conditioning systemwithno

hot water storage tanks integration was presented. Secondly,

the solar-driven hot water storage tanks applied to above

mentioned systemwere taken into deeper investigation.

3.1. Solar cooling mode with no storage system

In this paragraph we will study deeper the solar-assisted air-

conditioning system driven only by solar energy with the

main aim to underline the necessity of thermal storage inte-

gration. The air-conditioning is in operation during office

hours from 9 a.m. to 9 p.m., fromMonday to Friday. The basic

idea of this mode is to feed the absorption chiller with water

provided from solar collectors. The chiller has the minimum

and maximum generators inlet temperature of the order of

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 414482Fig. 1 e a) View of the CIESOL building with 160m flat-plate sola

facade, c) the nave with the main system components, d) hot wr collectors array, b) general view of CIESOL building-north

ater storage tanks.

we can use some auxiliary heater for example, but the

main purpose of this project is to maximize use of solar

thermal energy, avoiding the CO2 emissions to the

atmosphere, therefore the integration of the hot water

storage tanks is essential. To underline once more the huge

importance of integration of hot water storage tanks we

attempted to show that the solar-assisted air-conditioning

system could function only 28% of its total operating time

(from 9 a.m. to 9 p.m.) working with no thermal storage or

auxiliary heater (cf. Fig. 4).

3.2. Solar cooling mode with hot storage system

With the main goal to provide energy at moments when the

energy proceeding from solar collectors is insufficient, two

storage tanks were used. The use of a hot water tank between

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 4 144970 C and 95 C, respectively and out of those values the chillerwill stop working. Once the cooling operation is selected and

the leaving flat-plate collectors temperature is greater than

the minimum start-up temperature of absorption machine

(70 C), the chiller will function automatically and remain inoperation as long as there is a demand for chilled water.

During the period when absorption chiller is driven only by

solar energy valve V1 is opened while both valves V2, V3 are

kept closed and the pump P1 circulates the hot water between

the absorption chiller and solar collectors array, avoiding

storage tanks (c.f. Fig. 2). The bypass valve V4 is cycled ON and

OFF to control flow of heat medium through the generator in

response to the chilled water temperature. When the chilled

water temperature is satisfied, the V4 is closed, the cooling

water pump P2 will stopped after 4 min and the pump P3

will circulate the chilled water through the fan-coil units.

The system will remain in this state as long as the chilled

water increases due to increasing buildings load,

mg

Flat-plate

collectors array

Absorption

chiller

Hot water

pump

P1

Chilled water

pump P3

x

Teg

x

Tlg

me

x

x

Tee

Tle

Incident radiation

meter

x Tout

Cooling

tower

Cooling water

pump P2

xTsacTeac

x

mac

Cooling

load

STORAGE TANKS

T1 T2

V2 V4V1V3

Fig. 2 e The general scheme of the solar-assisted

air-conditioning system driven by solar energy.meanwhile the valve V1 modifies its opening level

depending on the difference between the entering and

leaving flat-plate collectors temperature. The pump P1

circulates the hot water between the absorption chiller and

solar collectors and no excess energy can be store due to

lack of the thermal storage.

To verify the necessity of thermal storage integration the

analysis of the leaving flat-plate collectors temperature has

been carried out since the absorption chiller is driven only by

solar energy. Taking into account the minimum generators

inlet temperature we focussed our study only on the leaving

collectors temperature superior than 70 C. The experi-mental data was collected during the cooling period of 2007

and 2008 with 1 min sampling period. Fig. 3 presents the

leaving flat-plate collectors temperature versus the incident

radiation and collectors mass flow rate. As can be seen in

the Fig. 3 for incident radiation less than 400 [Wm2] andcollectors mass flow rate higher than 9 [m3 h1] we cannotreach the leaving flat-plate collectors temperature higher

than 70 C (the minimum start-up temperature ofabsorption machine), thus the auxiliary heat source is

needed to start the cooling process. To fulfil this conditionFig. 3 e Leaving flat-plate temperature against collectorsthe solar collectors field and the absorptionmachine has been

reported to yield higher system efficiency and extends the

daily cooling period. It also prevents cycling of the absorption

machine due to variations in solar radiation intensity (Li and

Sumathy, 2000; Syed et al., 2005).

In this paragraph we want to point out the necessity of

storage tank integration, so we will focus on those modes of

systems operation that involve thermal storage. Energy can

be charged, stored and discharged daily or weekly depending

on the control strategy. In the morning and afternoon, when

the solar water is not sufficient to cover the cooling necessity,

the system is fed by water provided from hot storage

tanks. The control system compares the temperature of the

second storage tank to the minimum start-up temperature of

absorption machine (70 C) and if this temperature is graterthe chiller is switched on. During this operation mode valves

V1 is kept closedwhile valves V2, V3 are opened and the pump

P1 circulates the hot water between the absorption chiller and

storage tanks, avoiding solar collectors (cf. Fig. 2). In this way

the thermal energy is removing from storage tanks. The

process of thermal storage discharging will continue as long

as the second storage tanks temperature decreases below

the minimum generators inlet temperature. During the

period when the buildings load is low (low evaporatorsmass flow rate and incident radiation intensity during the

cooling period of 2007 and 2008.

experimental day (03/09/07) while the ambient temperature

reached 35 C. As we can see till 9:22 a.m. the evaporatorsleaving temperature was stable and of the order of 30 C.The solar-assisted air conditioning system was switched on

at 9:22 a.m. with the main goal to cover the cooling demand

in Ciesol building. The control system compared the

temperature of the second storage tank and the leaving

collectors to the minimum start-up temperature of the

absorption chiller. At that moment the solar hot water was

still too low and the warm water form storage tank was

used to driven the chiller. From 9:22 a.m. to 10 a.m. the

entering generators temperature presents the same profile

as the second storage tank temperature, indicating that the

absorption machine was provided with storage tanks.

During this period the chilled water gradually decreased and

reached the temperature of the order of 8 C. At the sametime the leaving collectors temperature increased sharply

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 41450temperatures) the control system demands low entering

generators temperature. In those moments only the amount

of solar heat necessary to cover the cooling loads is being

used and the excess solar energy is stored in two hot water

storage tanks. In this operation mode the pump P1 circulates

warmer water from solar collectors to the first tank and at

the same time the second storage tank is discharged to

supply the absorption chiller (c.f. Fig. 2). Another way of

storage tanks charging occurs when the chilled water is no

needed (weekends and lunch break 2O4 p.m.) and hot water

from solar collectors is used to load the thermal storage,

since the absorption chiller is switched off. In this mode the

Fig. 4 e Operation time of the solar cooling mode with no

storage tank versus the total operation time in cooling

period of 2007 and 2008.pump P1 circulates the hot water between solar collectors

and storage tanks.

Fig. 5 presents the temperature in the upper part of the

second storage tank, leaving collectors temperature,

generators entering temperature, ambient temperature and

evaporators leaving temperature against time for one

Fig. 5 e Generators entering temperature, evaporators leaving t

the upper part of the second storage tank and ambient temperadue to experimental modification of V1 opening level. Even

that the collectors temperature reached the value of about

75 C which would be enough to drive the chiller, the controlsystem indicated the storage tanks as a heat source because

of the high value of chilled water temperature. After 10 a.m.

we can observe that the leaving evaporators temperature

remains constant almost rest of the time due to low

buildings load. From 10 a.m. to 2 p.m. only amount of solar

heat necessary to cover the cooling demand was used and

the excess solar energy was stored in two hot water storage

tanks. Between 2 and 4 p.m. (lunch break) cold water was no

needed and solar water was used to load the hot water

storage tanks and it can be seen from Fig. 5 that the second

storage tank temperature increased from 75 C to about85 C. After 4 p.m. the chiller once again was switched on.Once more the control system compared the temperature of

the second storage tank and the leaving collectors to the

minimum start-up temperature of the absorption chiller.

Taking into account that the buildings load was low and the

control system demanded low entering generators

temperature, the absorption chiller was supplied with hot

water from second storage tank. At the same time the first

storage tank was charged with the hot water from solar

collectors. The system operated till 6:15 p.m.emperature, leaving collectors temperature, temperature in

ture against time (03/09/07).

Fig. 6 presents COP, obtained by applying Eq. (1), against

entering generators temperature, leaving evaporators

temperature and time for one experimental day (16/08/07).

During this operation day we had 100% of the buildings load

and the stable entering generators temperature of about

70 C. Although the buildings load was high (highevaporators temperatures) and the control system should

demand high entering generators temperature we set up the

optimum entering generators temperature of about 70 Cwith the main goal to maximize the use of storage system.

The solar-assisted air conditioning system was switched on

at 12:33 p.m. and operated till 5:00 p.m. and during this

period the COP varied from 0.31 to 0.84 at the beginning and

at the end of the cooling process, respectively. We can also

observe that the behaviour of the COP is almost constant

during all experiment. It is mainly due to the fact that the

absorption chiller was fed by water provided from second

storage tank while the warmer water from solar collectors

was used to charge the first tank. This operation mode

stabilizes the behaviour of the COP and prevents cycling of

neurons are set in layers, and thus a network is formed. Inputs

representing the variables that affect the output of the

network are feeding forward to each of the neurons in the

following layers with activation depending on their weighted

sum. Finally, an output can calculated as a function of the

weighted sum of the inputs and an additional factor, the

biases. The ability to learn is one of the outstanding charac-

teristics of an ANN. The weights of the inputs are adjusted to

produce a predicted output within specified errors. An ANN

system is characterized by its net topology, neuron activations

transfer, and learning method (Wang, 2001).

Theneuralnetworkselectedhere isamultilayer feedforward

perceptron (MLP) with one hidden layer. A tan-sigmoid transfer

function was used as the activation function for the hidden

layer, and a linear transfer function was used for the output

layer. The LevenbergeMarquardt (LM) algorithmwas applied as

themethod for achieving fast optimization (Chow et al., 2002).

4.2. ANN model development and results

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 4 14514.1. ANN model description

An artificial neural network (ANN) is massive interconnected,

parallel processing, dynamic system of interacting processing

elements that are in some aspect similar to the human brain.

The fundamental processing element is called the neuron,

which is analogous to the neural cell in human brain. Thethe absorption chiller that we can observe between

12:58O1:41 p.m. and 2:18O2:32 p.m. In those moments the

absorption chiller was provided with solar collectors and the

entering generators temperature varied due to variations in

solar radiation intensity. On other occasions we can observe

that the storage tanks have an essential importance of the

solar-assisted air-conditioning system.

4. ANN modelFig. 6 e Coefficient of performance of the absorption chiller agai

temperature and time (16/08/07).To determinate the ANN solar-assisted air-conditioning

systemsmodel provided from storage tanks, a set of 1250 data

points with a 1 min sampling period was used. Table 1

presents the input and output parameters used for training

the ANN absorption systems model. The input parameters

were monitored by data acquisition system from June 6,

2007 to September 25, 2007 and from July 14, 2008 to

September 15, 2008, and the outputs parameters were

estimated through the equation (c.f. Eq. (1)). In order to carry

out the network training, 1060 data patterns were used, and

the remaining 190 patterns were used as the test data set.

The training and testing data were normalized between

0 and 1, using (c.f. Eq. (2)):

xscaled x xmin=xmax xmin (2)where xmax and xmin are equal to themaximumandminimum

recorded values for each variable x. In order to determine thenst entering generators temperature, leaving evaporators

the number of input variables was varied. The selection of

the input variables was started with the configuration of 13

variables, and we progressively decreased the number of

variables by taking into account their importance. Finally,

we chose entering and leaving generators temperature,

entering and leaving evaporators temperature and the

second storage tanks average temperature as the more

favourable configuration of the network inputs.

After the input and output model variables were fixed, the

next step consists of determining the network architecture

Table 1 e Input and output parameters used for ANNabsorption systems model.

Input variables Range

Entering generators temperature [C] 64.8e90.7Leaving generators temperature [C] 38.7e84.5Generators mass flow rate [m3 h1] 7.3e16.8Entering evaporators temperature [C] 8.65e30.55Leaving evaporators temperature [C] 3.62e36.14Evaporators mass flow rate [m3 h1] 9.18e10.4Entering absorbers and condensers temperature [C] 24.02e41.3Leaving absorbers and condensers temperature [C] 24.77e43.64Absorbers and condensers mass flow rate [m3 h1] 0.07e45.08Incident radiation intensity [Wm2] 31.14e818.4Leaving flat-plate collectors temperature [C] 19e85.42First storage tanks average temperature [C] 59.24e81.49Second storage tanks average temperature [C] 62.87e87.45Coefficient of performance 0e0.99

Table 2 e RMSE errors of coefficient of performance andcooling capacity obtained during selection of inputsvariables.

Input variables RMSE [%]

COP Qcool

Teg, Tlg, mg, Tee, Tle, m

e, Teac, Tlac, m

ac, I, Tout, T1, T2 1.61 1.63


e, Teac, Tlac, I, Tout, T1, T2 1.33 1.06


e, I, Tout, T1, T2 1.56 0.93

Teg, Tlg, mg, Tee, Tle, I, Tout, T1, T2 2.26 2.33


e, I, Tout, T2 1.55 1.39

Teg, Tlg, Tee, Tle, me, I, Tout, T1, T2 3.49 1.46


e, Teac, Tlac, m

ac 2.36 1.23


e, I, Tout, T1 2.33 2.05


e, Teac, I, T2 1.78 1.67

Teg, Tlg, Tee, Tle, I, Tout, T1, T2 3.43 2.34


e, Teac, Tlac 1.83 1.80

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 41452ANN systems model, the neural network toolbox under the

Matlab environment was used.

When a large number of variables are eligible to be

included in a model, selecting optimal inputs becomes a crit-

ical step prior to the model development itself, since compu-

tational cost can be considerably reduced and because not all

the variables considered are always available (Bosch et al.,

2008). In this study, we attempt to predict the performance

of the absorption chiller provided from storage tanks with

the main aim of lowering the initial input parameters.

To verify the influence of the above mentioned input

parameters, we considered the RootMean Square Error (RMSE)

as a percentage of the mean measured values. In Table 2, one

can see the statistical results of RMSE errors of coefficient of

performance and cooling capacity obtained during selection

of inputs variables for the ANN solar-assisted air-

conditioning systems model.

Themain goal of this work is to determine the unique ANN

model with the minimal number of input patterns able to

estimate the two output variables. Through the analysis of the

results presented in the Table 2, we select themore favourable

Cooling capacity [Kw] 0.46e79.34configuration of ANN model input variables. To carry out the

network training, 1060 data patterns were used, where

Fig. 7 e RMSE evolution vs. the increase of hidden units.Teg, Tlg, Tee, Tle, I, Tout, T1, T2 3.43 2.17


e, I, Tout 2.52 1.96


e, I, T2 2.18 1.71

Tlg, mg, Tee, Tle, m

e, I, Tout, T2 12.65 2.58


e, T2 2.46 1.80


e, Teac 1.75 1.58


e, Tlac 2.91 2.12


e 4.40 3.77

Teg, Tle, I, Tout, T1, T2 25.52 24.6

Teg, Tlg, Tee, Tle, I, T2 4.68 3.21

Teg, Tlg, mg, Tee, Tle 4.22 3.41

Teg, Tlg, Tee, Tle, me 3.48 1.87

Teg, Tlg, Tee, Tle, T2 3.98 2.59

Teg, Tlg, Tee, Tle 4.63 3.24(Bosch et al., 2008). Several MLPs networks with different

numbers of hidden neurons Nh were trained. In order to

Fig. 8 e MBE evolution vs. the increase of hidden units.

assess the accuracy of the neuralmodelswe analyze the results

inmeaning of theRMSE andMeanBias Error (MBE) expressed as

values are equal to 1 while the intercept values are very small.

We can observe that the training values resulting in a good

match to the experimental values.

Fig. 9 e ANN architecture used for the absorption chiller

system provided with two storage tanks.

Fig. 11 e Comparison of actual and ANN-predicted values

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 4 1453apercentageof themeanmeasured. Figs.7and8 illustrateRMSE

and MBE evaluation versus the increasing number of hidden

neurons, respectively. As can be seen for numbers of hidden

neurons higher than 9, the RMSE became almost constant,

and the maximum and minimum deviations were found 0.023

and -0.034, respectively. Finally, the architecture 5-9-2 (5

inputs, 9 hidden and 2 output neurons) appears to be the most

optimal topology.

Fig. 9 presents the configuration of the two-layer back

propagation network selected in this work. The input layer

includes Teg, Tlg, Tee, Tle, and T2. The hidden layer has nine

nodes, and the output layer includes COP andQcool.

Once the training process was finished, we proceeded to

compare the predicted values from ANN model with actual

values. We used a set of 190 patterns as the test data. The

accuracy of the ANN model was evaluated on the basis of theFig. 10 e Comparison of actual and ANN-predicted values

of COP for the absorption chiller provided with two storage

tanks.regression analysis of estimated versus measured values, in

terms of the slope e a, intercept e b of the linear fit, the

determination coefficient - R, RMSE and MBE. Figs. 10 and 11

present the comparison between the actual and predicted

values of COP and Qcool, for absorption system, respectively.

The actual COP and Qcool were calculated as explained in

paragraph 2. The aforementioned above figures show that the

majority of the experimental points are located over the

perfect adjust line 1:1, illustrating minimal dispersion.

The RMSE error caused by the network in every case is less

than 0.70%, and the deviation is practically null. It is noted

that R values are equal and reach around 0.99, and all the slope

of cooling capacity for the absorption chiller provided with

two storage tanks.5. Conclusions

The main objective of this study was to emphasize the great

importance of hot water storage tanks integration in a solar-

assisted air-conditioning system. The storage tanks system is

very useful not only in the clods alternation when the solar

hot water temperature is too low to cover the absorption

machine demand but especially in the starter moments in the

morning and whenever the incident radiation is minor than

400 [Wm2]. It has been observed the solar-assisted air-conditioning system could function only 28% of its total

operating time (from9 a.m. to 9 p.m.) workingwith no thermal

storage. In this work, real data of an operating solar-assisted

air-conditioning system provided from storage tanks was

used to derive an ANN systems model to predict the coeffi-

cient of performance and cooling capacity of the absorption

chiller. The main aim of the present study was to determine

the unique ANN model with a minimal number of input

variables. Finally, the total of five variables were used as the

more favourable configuration of the network inputs -

entering and leaving generators temperature, entering and

leaving evaporators temperature and the second storage

tanks temperature. Results demonstrate accurate predictions

from the ANN model, yielding an RMSE less than 0.70% and

practically null deviation, which can be considered very

satisfactory. Results obtained through this prediction are very

useful for control and monitoring strategies. The selection of

the ANNs inputs permits reduction of the monitoring costs,

since we are able to avoid a lot of redundant measurements

points. In this work we determined a methodology, which

could be easily adapted to other systemswith themain goal to

improve the design of solar-assisted systems making them

more attractive to potential users. Considering obtained

acceptable results, we point out that future study in this field

should focus on the use of the artificial neural networks to

(MEC). The authors would like to thank to all companies and

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Chow, T.T., Zhang, G.Q., Lin, Z., Song, C.L., 2002. Globaloptimization of absorption chiller system by genetic algorithmand neural network. Energy Build. 34, 103e109.

Helm, M., Keil, C., Hiebler, S., Mehling, H., Schweigler, C., 2009.Solar heating and cooling system with absorption chiller andlow heat storage: energetic performance and operationalexperience. Int. J. Refrigeration 32, 596e606.

Henning, H.M., 2004. Solar-assisted Air-Conditioning in Buildings.A Handbook for Planners. Springer Wieb, New York.

Kreider, J.F., Kreith, F., 1981. Solar system for space cooling. In:Solar Energy Handbook. McGraw-Hill, New York.

Lopez, G., Rubio, M.A., Martinez, M., Batlles, F.J., 2001. Estimationof hourly global photosynthetically active radiation usingartificial neural network models. Agric. For. Meteorol. 107,279e291.

i n t e r n a t i o n a l j o u r n a l o f r e f r i g e r a t i o n 3 4 ( 2 0 1 1 ) 1 4 4 6e1 4 5 41454InfanteFerreira,C.A., 2006. Solar cooling.AnoverviewofEuropeanapplications and design guidelines. ASHRAE J., 48. June 2006.

Bosch, J.L., Lopez, G., Batlles, F.J., 2008. Daily solar irradiationestimation over a mountainous area using artificial neuralnetworks. Renew. Energy 33, 1622e1628.institutions included in PSE-ARFRISOL project.

r e f e r e n c e s

Asdrubali, F., Grignaffini, S., 2005. Experimental evaluation of theperformances of a H2O-LiBr absorption refrigerator underdifferent service conditions. Int. J. Refrigeration 28, 489e497.

Balaras, C.A., Henning, H.M.,Wiemken, E., Grossman, G., Podesser, E.,predict the global systems efficiency and energy usewhen the

system works at all operation modes, considering the energy

provided from all heat sources.

Acknowledgements

This research has been carried out with the help of the project

PSE-ARFRISOL (The Singular Strategic Project called Biocli-

matic Architecture and Solar Cooling), PS-120000-2005-1

financed by the Spanish Ministry of Education and ScienceLopez, G., Batlles, F.J., Tovar-Pescador, J., 2005. Selection of inputparameters to model direct solar irradiance by using artificialneural networks. Energy 30, 1675e1684.

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Performance study of solar-assisted air-conditioning system provided with storage tanks using artificial neural networks1 Introduction2 Description of solar-assisted air-conditioning system3 Integration of the hot water storage tanks3.1 Solar cooling mode with no storage system3.2 Solar cooling mode with hot storage system

4 ANN model4.1 ANN model description4.2 ANN model development and results

5 Conclusions Acknowledgements References

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