Performance evaluation of a ground source heat pump system based on ANN. and ANFIS models

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1 Performance evaluation of a ground ource heat pump ytem baed on ANN and ANFIS model eijuan SUN a Pingfang HU a * Fei Lei a Na Zhu a Jiangning Zhang a a Huazhong Univerity of Science and echnology uhan P. R. China Abtract: he aim of thi work i to calculate the heat pump coefficient of performance () and the ytem of a ground ource heat pump (GSHP) ytem baed on an artificial neural network (ANN) model and (adaptive neuro-fuzzy inference ytem (ANFIS) model. In order to get the training and tet data a GSHP ytem of an office building in China wa monitored in the ummer of 013 and the ytem ue a GSHP unit and a water chiller unit a the cooling ource. o calculate the heat pump the water temperature entering/exiting the condener and the water temperature entering/exiting the evaporator of the GSHP unit were ued a input layer of ANN and ANFIS model. hile ix parameter including the water temperature entering/exiting the condener of two unit and the water temperature entering/exiting the uer ide were ued a the input layer. Some tatitical method were adopted to evaluate the accuracy of the calculation model. he reult how that the model provide high accuracy and reliability for calculating performance indexe of GSHP ytem with fewer parameter. Keyword: Artificial neural network ANFIS Ground ource heat pump 1. Introduction GSHP ue the low grade energy tored in the hallow level of the earth to heat or cool the building. It ha been applied to real project widely thee year thank to it high energy efficiency energy-aving environment-friendly and the ability to ue the low level energy [1]. Calculation of the real-time evaluation indexe i important for ytem evaluation ytem optimization and fault diagnoi of the GSHP ytem. But for mot of the GSHP project which have been put in operation the monitoring ytem ha t been intalled. It i hard to get all the parameter needed when we calculate the evaluate indexe uch a coefficient of performance() becaue of the lack of the meauring device in the ytem or the diagnoi of the enor or other reaon. Some author have tried calculating the evaluation indexe of ome ytem or equipment with low cot parameter while ANN and ANFIS are ued frequently in the reearche. Artificial neural network i an information proceing idea that i inpired by the way of biological nervou ytem uch a the brain proceing information[]. It can tackle complex problem in actual ituation with the advantage of of learning ability memory imulating and nonlinear approximation. ANFIS i created combining neural network and fuzzy model it ha the advantage of the fuzzy proceing elf learning and the nonlinear approximation. H.M. Ertunc calculated the winter of a horizontal GSHP baed on ANN model uing the * Correponding author. el: ; fax: ; addre: pingfanghu1@163.com pingfanghu@hut.edu.cn (P.Hu)

2 air temperature leaving/entering the condener the water temperature leaving/entering the evaporator and the ground temperature a the input layer parameter[3]. Halinda [4] built an ANN model for a tandard air-conditioning ytem of a paenger car to calculate the cooling Nomenclature water denity ( kg/ m 3 ) c pecific heat capacity of water ( kj/( kg K) ) m g 3 water ma flow on the evaporator ide of the GSHP unit ( m / h ) m l 3 water ma flow on the load ide of air conditioning ytem ( m / h ) gevap i evaporator inlet water temperature of the GSHP unit ( o C) gevap o evaporator out water temperature of the GSHP unit ( o C) gcond i condener inlet water temperature of the GSHP unit ( o C) gcond o condener outlet water temperature of the GSHP unit ( o C) cevap i evaporator inlet water temperature of the chilled water unit ( o C) cevap o evaporator out water temperature of the chilled water unit ( o C) ccond i condener inlet water temperature of the chilled water unit ( o C) ccond o condener outlet water temperature of the chilled water unit ( o C) g power conumed by the GSHP unit ( ) c power conumed by the chilled water unit ( ) power conumed by the water pump on load ide ( ) l p power conumed by the water pump on ground ource ide ( ) g p

3 ct p power conumed by the water pump on cooling tower ide ( ) l i inlet water temperature on the load ide ( ) l o outlet water temperature on the load ide ( ) capacity compreor power input and the coefficient of performance () of the automotive air-conditioning (AAC) ytem. he input layer parameter of ANN model are the compreor peed air temperature at evaporator inlet air temperature at condener inletand air velocity at evaporator inlet. Arzu S [5] calculated the of a ingle tage vapor compreion refrigeration ytem with inner heat exchanger baed on ANN model and ANFIS model uing the evaporator temperature condener temperature ub cooling temperature uperheating temperature and cooling capacity. ZHAO Jing [67] made a pot evaluation before the renovation of a central air-conditioning ytem of a large-cale public building and a prediction evaluation after the retrofit cheme i determined. he prediction evaluation model wa built baed on Back-Propagation Artificial Neural Network by the ue of MALAB Neural Network oolbox. ANN and ANFIS are applied in many field. In the HVAC area they have been ued in load prediction energy management and controlling ytem fault diagnoi ytem identification and other apect [8].he aim of thi thei i to calculate the heat pump and the ytem of the GSHP baed on ANN and ANFIS model through a low number et of data. For getting the training and tet data a GSHP ytem of an office building in China wa teted in the ummer of Sytem decription and monitoring data analyi.1 Sytem decription In thi tudy a GSHP ytem of an office building wa monitored for a cooling eaon in ummer of 013. he office building wa located in Shaoxing China. he cooling load and heating load of the office building (air conditioning area: 730 m ) were 618 k and 403k at deign condition repectively. he chematic diagram of the air conditioning ytem for cooling mode i illutrated in Fig1. wo heat pump were were elected a the cooling ource according to the heating and cooling load one i a total heat recovery GSHP unit (cooling capacity: 315k; heating capacity: 343.7k; : 5.78 and 4.48 in cooling eaon and heating eaon repectively) and the other on i a water chiller unit (cooling capacity: 307k; heating capacity: 36k; : 5.45 and 4.3 in cooling eaon and heating eaon repectively). wo unit can run imultaneouly or run by itelf depending on the requirement. he total recovery GCHP unit upplie dometic hot water. he total heat recovery GSHP unit rejected heat to the ground by mean of buried pipe while the water chiller unit diipate heat through cooling tower. Moreover the air conditioning ytem include three water pump on ource ide a water pump on cooling tower ide three water pimp on uer ide two hot water pump and a heat inulating water tank. 140 borehole were deigned. he drilling diameter borehole pacing and the borehole depth were 150mm 4m and 50m repectively. Single-U double-u and three-u heat exchanger were elected and the outer diameter of pipe i 3mm.

4 he water return from air conditioning terminal releae heat to the refrigerating fluid in the evaporator of the GSHP unit or the water chiller unit and then goe back to the air conditioning terminal to cooling the houe. he circulating water from the buried pipe extract the heat from the refrigerating fluid in the condener of the GSHP unit and reject the heat to ground. he cooling water from the cooling tower aborb heat from the refrigerating fluid in the condener of the water chilled unit and i cooled by cooling tower. Some meauring device were intalled in the ytem to get running data. he water temperature entering and exiting condener evaporator and the air conditioning terminal were meaured by temperature enor. he flow rate of water entering and exiting condener evaporator and the air conditioning terminal were meaured by flow enor. Both the temperature enor and flow enor were intalled with wirele tranmitting module which can tranmit the data collected. irele mart electric meter were ued to meaure the power conumed by cooling tower GSHP unit water chilled unit and water pump the data alo tranmitted by warele tranmitting module. he wirele ignal acquiition module collect data from wirele tranmitting module and upply them to the local ever through internet or local area network. hen we can get the data from the monitoring platform of local ever.. Monitoring data analyi Fig.1. Schematic diagram of the air conditioning ytem for cooling mode

5 he GSHP in Shaoxing wa monitored every 15 minute during normal office hour from June 1 t to June 7 t 013. e had obtained 79 data pattern total in thee 7 day. he of GSHP unit wa obtained by Eq.(1) and the of the GSHP air conditioning ytem wa calculated by Eq.() to verify the reult from ANN and ANFIS approach. Qg cl (1) g here the cooling capacity of the GSHP unit Q g cl i defined a Eq.() Q cm ) () g cl g ( gevap i gevap o g Q c i p (3) here Q i the cooling capacity of the airconditioning ytem defined a Eq.(4) and p i the total power conumed by all the water pump defined a Eq.(5) i Q cm l( l o l i ) (4) i p l p g p ct p (5) During the monitoring period the GSHP unit wa running all the time but the chilled water unit wa intermittent running. he meaured running data value of the GSHP air conditioning ytem are given in Fig. -4. Fig. how the variation of gevap o gcond i and gcond o of gevap i the GSHP unit with it. e can ee that gevap i gevap o gcond i and gcond o variate in 9.6~17.1 o C 8.0~14.3 o C 8.9~40.7 o C and 5.7~37. o C. of the GSHP unit i between.5~5.4. he mean difference of gevap i and gevap o i.33 o C for gcond i and gcond o it i.7 o C. of the GSHP unit how an oppoite direction of changing trend approximately with the changing trend of gcond i and gcond o. It indicate that in a certain range the lower of the output water temperature of ground ource well the higher of GSHP unit which mean the better operation performance of the GSHP unit. Fig. 3 how the variation of cevap i cevap o ccond i ccond o and c of the chilled water unit. he power conumed by the chilled water unit mean that the running tate of thi unit i off. Fig.4 how the variation of l i l o and the ytem. e can ee

6 that l i and l o variate in 8.~14.3 o C 9.5~17.0 o C. he ytem i between 1.3~4.0. he mean difference of l i and l o i.1 o C. Fig.. he variation of water temperature of GSHP unit Fig.3. he variation of water temperature power conumption of chilled water unit

7 Fig.4. he variation of water temperature on the load ide and the ytem 3. Artificial neural network (ANN) and adaptive neuro-fuzzy inference ytem (ANFIS) 3.1 Artificial neural network (ANN) ANN i interconnected by lot of join (or named a neuron) which are nonlinear information proceing unit with multiple input and ingle output. Every point tand for an activation function which can be defined in many way uch a threhold function igmoid function and hyperbolic tangent function [9]. ANN operate much a a black box model requiring no detailed information about the ytem. On the other hand they learn the relationhip between the input and the output baed on the training data [10]. here are numerou algorithm available for training neural network model. he mot popular one i the back propagation algorithm which ha different variant. Standard back propagation i a gradient decent algorithm. A typical BP artificial neural network tructure i illutrated by Fig.4. It include three layer: input layer hidden layer and the output layer. he input layer receive the input ignal by weight and tranfer the proceed ignal to the hidden layer; the hidden layer i inner proceing layer which convere the information it can be claified a ingle hidden layer and multiple hidden layer the lat hidden layer export information to the output layer. After a further proceing a forward propagation learning proce i completed. he output layer export the information proceing reult but if the actual output i not agree with the expected reult ANN will tep into the mean quare error (MSE) back propagation tage. he cycle of information forward propagation and error back propagation i a adjutment proce of each layer weight it i alo the learning proce of ANN. he proce won t top until the MSE decreae to an acceptable level or the training epoch are reached.

8 input output input layer hidden layer output layer Fig.5. A typical BP artificial neural network tructure 3. Adaptive neuro-fuzzy inference ytem (ANFIS) he Adaptive neuro-fuzzy inference ytem (ANFIS) wa came up with by Jang Roger it function i imilar with the firt order Sugeno fuzzy inference ytem. ANFIS combined the elf-learning ability of neural network and the advantage of fuzzy inference together being able to approximating any linear or nonlinear ytem [33]. ANFIS i baed on data et rather than experience or intuition the memberhip function and the fuzzy rule are obtained by learning with the data et. hi i important for the complex ytem or the ytem whoe propertie are not realized fully by people [34]. he typical tructure of ANFIS i illutrated by Fig.5 [35] the memberhip function of one layer are ame (et the output of the layer of i a output. O ) x y i the input of the ytem and f i the l i Fig.6. he tructure of a typical ANFIS model he firt layer: the input ignal are fuzzified by the node in thi layer.

9 O1 i Ai( x) i 1 O1 i y) i 3 4 (5) ( Bj here A/B are fuzzy et O l i i the memberhip function of the fuzzy et the default i bell-hape. O he econd layer: the node in thi layer i to calculate the fitne of each rule ( x) ( y) i 1 (6) i Ai Bi he third layer: normalize the fitne of each rule. /( ) i 1 (7) O3 i 1 O he fourth layer: calculate the output of each rule f ( p x q y r ) i 1 (8) 4 i i i i i he fifth layer: there i only one node in thi layer which can calculate the final output of the ytem. O fi i y fi i 1 (9) i 5 i i A hybrid algorithm which combined the black propagation and the tet quare method together i adopted to train ANFIS it can help the ytem to model the data et. 4. ANN and ANFIS model for cae tudy he ANN and ANFIS model for calculating the GSHP unit i illutrated in Fig.6. he ANN and ANFIS have the ame parameter gevap i gevap o gcond i and gcond o in the put layer and the ame parameter in the output layer. he ANN and ANFIS model for calculating the ytem i illutrated in Fig.7. he ANN and ANFIS have the ame parameter gcond i gcond o ccond i and ccond o in the put layer and the ame parameter in the output layer. and ccond o value are et a 0 when the running tate of the chilled water unit i off which ccond i mean thee value make no difference to. Both for the model for calculating the GSHP unit and the ytem 44 data pattern from July 1 t to July 16 t elected from the total 79 data pattern were ued for training the ANN model the total data pattern included 1030 pattern from July 1 t to July 8 t were ued for teting the trained ANN model.

10 gevap i gevap o gcond i gcond o ANN or ANFIS model Fig.7. ANN and ANFIS model for calculating GSHP unit gcond i gcond o ccond i ccond o l i l o ANN or ANFIS model Fig.8. ANN and ANFIS model for calculating ytem m n l (9) m nl (10) 1 m ( n l) t (11) here i the number of neuron on hidden layer; i the number of neuron on input layer; l i the number of neuron on output layer; i a contant value between 0~10 and i the number of training data pattern. For ANFIS model three memberhip function named Gaumf are choen the training epoch i 600 and the MSE i 0 other parameter are default value. ANN and ANFIS model were programmed in the MALAB 7.0 environment. m he main training program of ANN model i a following: [Inputmintraininputmaxtraininput] = premnmx(traininput); % Normalize the input data net=newff(minmax(input)[141]{'tanig''purelin'}'trainlm'); % Build an ANN net.trainparam.epoch=5000; % Set the training epoch a 5000 n t

11 net.trainparam.goal=1e-06; % he training goal i 1E-07 [nettr]=train(netinputt); % rain the net ave net % Save the net he main teting program of ANN model i a following: [etinput] = tramnmx(tetinputmintraininputmaxtraininput); % Normalize the input data load net % Load the trained net output=im(netetinput); % Get the output by the net he main program of ANFIS model i a following: fimat=genfi1(trndata[nummfs1nummfsnummfs3]char(mfype1mfypemfype3)'co ntant'); % generate the initial ANFIS uing meh cutting method [Fi error tepize chkfi chker]=anfi(trndatafimattrnoptdioptchkdata); % train the model baed on training data anfi_cop=evalfi(chkdata1chkfi); output we need % input the teting data to the trained ANFIS and get the Some tatitical method abolute error relative error root-mean quared (RMS) abolute fraction of variance ( ) were adopted to validate the model they were obtained by Eq(1)-(15) repectively. he maller of abolute error root-mean quared (RMS)and relative error R the more accurate of the ANN model. Abolute fraction of variance ( of (0~1) value of 1 denote perfect model. x x calculate m actual m R ) wa in the range (1) xcalculate mx actual m (13) x actual m n ( m xcalcu m x RMS n 1 actual m) (14) n m 1 ( xcalcu m x R 1 n ( x ) m 1 actual m ) actual m (15) here x i the value calculated byann model; x actual m i the actual value; calculatem n i the number of data pattern. 5. Reult and dicuion

12 Suitable ANN and ANFIS model were developed by trail and error. Fig. 8-9 are the plot of GSHP calculated by the ANN and ANFIS model v. the correponding value obtained from experiment repectively. Fig are the plot of ytem calculated by the ANN and ANFIS model v. the correponding value obtained from experiment repectively. And for the ytem the tatitical value are howed in able. Fig.9. Comparion of actual and ANN calculated value of the GSHP Fig.10. Comparion of actual and ANFIS calculated value of the GSHP

13 Fig.11. Comparion of actual and ANN calculated value of the ytem Fig.11. Comparion of actual and ANFIS calculated value of the ytem able 1 Comparion of ANN and ANFIS tatitical value for calculated GSHP RMS R ANN ~0.6-9%~7% ANFIS ~0.3-10%~9%

14 able 1 Comparion of ANN and ANFIS tatitical value for calculated ytem RMS R ANN ~ %~10% ANFIS ~0.18-9%~7.8% Fig how that calculated value by ANN and ANFIS model agree well with the actual value. Once been trained the model can calculate the GSHP and the ytem in high preciion jut baed on fewer parameter. hat more the model have high calculation accuracy not only for the trained data pattern but alo for the untrained data pattern which how they have an excellent capability of generalization. For calculating the GSHP unit the propertie of the ANN and ANFIS model which are evaluated by tatitical value are tabulated in able 1. he relative error of ANN model are all within 10% and the relative error of 94.% data pattern are within 5%. And for ANFIS model the relative error of all the data pattern are within 10% too and of 9.% data pattern are within 5%. For calculating the ytem the propertie of the ANN and ANFIS model which are evaluated by tatitical value are tabulated in able. he relative error of all the data pattern are within 10% for both ANN model and ANFIS model. he relative error of 89.6% data pattern are within 5% for both ANN model and it i 94.9% for ANFIS model. he relative error are all within the acceptable limit. From able 1 and we can ee that ANN and ANFIS model both have a good calculation accuracy. the relative error and abolute error of them are all very low. hether for the heat pump or for the ytem ANFIS model alway have a higher accuracy than ANN model the abolute error the relative error and RMS are maller the ANFIS model i more appropriate to calculate the GSHP and the ytem fewer parameter. 6. Concluion R i cloer to1 it prove that baed on In thi thei an ANN and an ANFIS model were built baed on fewer parameter to calculate the important performance evaluation indexe: the GSHP and ytem of the GSHP ytem. o obtain the training and tet data for the calculation model a GSHP air conditioning ytem were monitored in a cooling eaon. One GSHP unit and a chilled water unit connect in parallel in the ytem. For the ANN and ANFIS model for calculating the GSHP the parameter in the input layer are: the evaporator inlet and outlet water temperature of the GSHP unit and the condener inlet and outlet water temperature of the GSHP unit. For the ANN and ANFIS model calculating the ytem ix parameter are in the input layer: the condener inlet and

15 outlet water temperature of the GSHP unit the the condener inlet and outlet water temperature of the chilled water unit and the inlet and outlet water temperature on the load ide of the ytem. 44 data pattern are ued a training data and all the data pattern (79 data pattern) are ued a tet data. he trained model can calculate the performance indexe of the GSHP ytem. Some tatitical method abolute error relative error root-mean quared (RMS) abolute fraction of variance ( ) were adopted to validate the model. he reult how that the ANN and ANFIS R model are capable to calculate the GSHP unit and the ytem baed on fewer parameter and ANFIS model how better performance. Acknowledgment with a good accuracy he author would like to acknowledge the upport from the National Natural Science Foundation of China (Grant NO ) Reference [1] N.R DIAO and Z.H FANG "Ground coupled heat pump technology" Beijing: Higher Education Pre.006:4-6. [] Z.X GE and Z.Q SUN "Neural network theory and MALAB 007 realization" Beijing: Indutry Publihing Houe 007. [3] H. Een M. Inalli A. Sengur and M. Een "Forecating of a ground-coupled heat ump performance uing neural network with tatitical data weighting pre-proceing" International Journal of hermal Science vol. 47 pp [4] H. M. Kamar R. Ahmad N. B. Kamah and A. F. Mohamad Mutafa "Artificial neural network for automotive air-conditioning ytem performance prediction" Applied hermal Engineering vol. 50 pp [5] Ş. Şahin "Performance analyi of ingle-tage refrigeration ytem with internal heat exchanger uing neural network and neuro-fuzzy" Renewable Energy vol. 36 pp [6] C. Chang J. Zhao and N. Zhu "Energy aving effect prediction and pot evaluation of air-conditioning ytem in public building" Energy and Building vol. 43 pp [7] J ZHAO "echnological ytem on energy efficiency diagnoi and evaluation for large-cale public building": ianjin Univerity010. [8] Y.Y LI and Y.J ANG "Artificial neural network (ANN) in HVAC area" HVAC. pp [9] C.H DONG" MALAB neural network and application" Beijing: National Defene Indutry Pre 005. [10] S. A. Kalogirou "Application of artificial neural network in energy ytem" Energy Converion and Management vol. 40 pp [11] J. S. R. Jang "ANFIS: adaptive-network-baed fuzzy inference ytem" Sytem Man and Cybernetic IEEE ranaction on vol. 3 pp [1] X.Y LIU and H.X PAN "Realization of Self- adaptive Fuzzy- neural Control Sytem Baed on MALAB" Mechanical Engineering & Automation pp [13] Z.X ZHANG C.Z SUN and (Japan) E.MIZIZUANI "Neural-fuzzy and oft computing" ZHANG P.AGAO C.H. Xi an: Xi an Jiaotong Univerity Pre

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