Neural Network Application for Geothermal Heat Pump Electrical Load Prediction

Neural Network Application for Geothermal Heat Pump Electrical Load Prediction

SatrioAnindito*, Eun-ChulKang**, Euy-JoonLee**

*RenewableEnergyEngineering,UniversityofScienceandTechnology(satrio@kier.re.kr)

**KoreaInstituteofEnergyResearch(kec8008@kier.re.kr),

**KoreaInstituteofEnergyResearch(ejlee@kier.re.kr)

지열 히트펌프 전기부하 예측을 위한 신경망 적용 방법

사트리오 아닌디토*,강은철**,이의준**

*과학기술연합대학원대학교 재생에너지공학

**한국에너지기술연구원 에너지효율연구단

Abstract

신경망방법은 공학,경영 그리고 정보기술과 같이 다양한 분양에서 널리 사용되어지고 있다.신경망방법은 기본적 으로 예측,제어,식별과 같은 기능을 가지고 있는데,본 논문에서는 신경망방법을 이용하여 C사의 모델 T의 히트 펌프 전기부하를 예측하였다.부하예측은 시스템을 더욱 효율적이고,적절하게 만들기 위해 필요하다.본 논문에서 사용된 히트펌프는 지열원 히트 펌프 시스템이다.이 지열 히트 펌프의 부하는 사전에 미리 예측되어진 외기온도 및 건물 열부하에 따라 측정 학습된 전력 소비량으로 겨울에는 난방,여름에는 냉방에 대한 전력 부하를 예측할 수 있다.

이 신경망방법은 신경망 학습 순서를 통해 부하 예측을 위해 히트펌프의 성능데이터를 필요로 한다.이 부하 예측 인공지능망 방법으로 외기 온도별 건물 통합형 지열 히트 펌프 부하가 예측되어질 수 있다.

Keywords:NeuralNetwork(신경망),GeothermalHeatPump(지열 히트 펌프),

ElectricalPowerConsumption(전력 소모량),OutdoorTemperature(외기온도)

Nomenclature

COP :Coefficientofperformance E :Costfuntion

EER :Energyefficiencyratio f :activationfunction

Ii :Inputvalueofneuralnetwork k :Numberofiteration(epoch)

Od:Thej^thcomponentofthedesiredoutput Sj:The outputofj^thneuron from the

lasthiddenlayer

[논문] 한국태양에너지학회 논문집 Journal of the Korean Solar Energy Society

Vol. 32, No. 3, 2012 IS S N 1 5 9 8 - 6 4 1 1

Submitdate:2012.2.8, Judgmentdate:2012.2.12, Publicationdecidedate:2012.5.22 Communicationauthor:EuyJoonLee(ejlee@kier.re.kr)

Wi :Valueofweight

y :Outputvalueofneuralnetwork yd :Thej^thcomponentoftheANNoutput η :Learningrate

1.Introduction

Geothermalheatpump(GHP)orground source heatpump (GSHP)is appropriate device to keep temperature.Ground can keep temperature more consistent than others,duetotheabilityinabsorbing46% heatenergy insideofitand itsbuffering characteristics.Moreover,the heatpump operatesusingthesamecycleasrefrigerator. ThiscanbeseenatFig.1.Thesignificant difference between a ground source heat pumpandarefrigeratoristhattheground sourceheatpumpismeanttoruninboth directions.Whenincoolingmode,theearth connection to refrigerant heat exchanger becomesthecondenser,andtherefrigerant- to-airheatexchangerbecomestheevaporator¹⁾.

Fig.1Refrigerationcycleinheatingmode

Load prediction is needed for making

1)Clean Energy ProjectAnalysis:RETScreen Engineering & Cases Textbook Ground-Source Heat Pump Project Analysis Chapter, MinisterofNaturalResourcesCanada;2001-2005,pp.11,47,57

planning,strategies,anddecision.Itcannot changewhatwillhappeninthefuturethat comefrom nature,butitcanhelptoprepare facing it.InGeothermalheatpump(GHP) field,aloadpredictioncanbeusedtoget information about electrical load in the futuredependingupontheoutdoortemperature. The value of GHP thermal load very depends on weathercondition and itwill alsoeffectthevalueofelectricalloadwhich willbe consumed.The predictorcan not changetheweathercondition,butitcanbe the guidance for the decision maker to decidehow much electricalsourceshould beprovidedforGHPsystem.JanKreider²⁾ developed the artificial neural network (ANN)modeland this paperutilizes his modeltopredictelectricalloadpredictorof aGHP basedonarealmodeldata.

2.ANN modeling

An artificialneuralnetwork(ANN)is a mathematicalorcomputationalmodelthat isinspiredbythestructureandfunctional aspects ofbiologicalneuralnetworks.A neuralnetworkconsistsofaninterconnected groupofartificialneurons,anditprocesses informationusingaconnectionistapproach forcomputation.InmostcasesANN isan adaptivesystem thatchangesitsstructure based on externalorinternalinformation thatflowsthroughthenetworkduringthe learningphase.Modernneuralnetworksare non-linearstatisticaldata modeling tools. They are usually used to modelcomplex

2)Kreider,K.F.,andJ.S.Haberl.Predictinghourlybuildingenergyuse:

Thegreatenergypredictorshootout-Overview anddiscussionof result,1994. ASHRAETransactions100(2),pp1104-1118,1994.

relationshipsbetweeninputsandoutputsor tofind patternsin data.ANN isbuiltby someneurallayersandeachlayerconsists ofseveralnodes(neurons)whichhavethe calculation function.Simple modelneuron canbeseeninFig.2.

Fig.2A neuronmodel

W1statesthevalueofweightsW1,W2,… Wn.Ontheotherhand,_ statesthevalue ofinputsI1,I2,…Inｆ isactivationfunction, hasaroleastriggeroutputoftheneuron, meanwhile the wb is the bias. It is necessary to adjustthevalueresultfrom theactivationfunctionｆ.Thismodelcan beexpressedbyotherform,Eq.1

  

  



___ (1)

WeightsaretheparametersoftheANN.

Their value can be updated at every iteration.Updatingthevalueofweightsis thecoreoftheANN learningprocess.This ANN learningprocessusesbackpropagation learning algorithm.Thecostfunction that isoftenusedisthesum ofsquarederrors (SSE),statedbyEq.2

_{ }



_ ^{  (2)}

Where Od is the j^th componentofthe desiredoutput,whileydisthej^thcomponent oftheANN output.Tominimizethevalue of error,the ANN weights are trained accordingtotheEq.3.

   _{   }





(3)

Attheoutputlayer,thegradientofcost functiontoweight,Eq.4

_^







_ __^

_

(4)

Where_^ istheweightthatconnects thed^thneuronfrom outputlayerwiththej^th neuronfrom thelasthiddenlayer.Forthe hiddenlayer,thegradientequationis,Eq.5

_^







_ __^

_





_ _′__^

_

(5)

_^ istheweightthatconnectsthej^th neuron from thelasthidden layerwithi^th neuronfrom thebehindhiddenlayerandSj

is the outputofj^thneuron from the last hiddenlayer³⁾.

3)Anindito,S.,Hadisupadmo,S.,Samsi,A.,DesignandImplementation ofWater Leveland Temperature Controller by Using Artificial NeuralNetworkofMini-PlantProcess,FinalYearProject-Physics Engineering-BandungInstituteofTechnology,2009,pp.12-13

3.ANN PredictorDesign

TheANN predictorisdesignedtohave someinputsandoutputs.Theseinputswill be processed with ANN calculation,and then generate 2 actual outputs. These actualoutputswillbecompared with the desired outputs to generate errors which willbeusedinbackpropagationcalculation toimprovetheweights.Theblockdiagram ofANN designcanbeseenatFig.3.

Fig.3Blockdiagram ofneuralnetworkpredictor

Fig.4Thestructureofneuralnetworkpredictor

Fig.4showsthestructureofANNpredictor. TheANN predictorhas3inputswhichare hour ofyear(h),outside temperature(°C), Thermalload(kWt)and 2 outputs which areelectricalpowerconsumption24hours ahead(kWe)andCOP24hoursahead.The parameterofelectricalpowerconsumption 24hoursaheadisthepointofthispaper. Table1istheparameterthatispredicted

withthisneuralnetwork.Besidesthat,this ANN predictoruses1hiddenlayerwith4 nodesinside.

Parameters Role HourofYear(h) Input OutsideTemp(°C) Input ThermalLoad(kWt) Input

ElectricalPower Consumption24hours

ahead(kWe)

Output

COP/EER 24hoursahead Output Table1.Parametersofneuralnetwork

Moreover,itusessigmoid asactivation function.Eq.6

     ^{ }

 (6)

The result of sigmoid calculation is alwaysbetween0and1,thereforethatresults must be multiplied with the maximum value ofeach parameter.The maximum valuecanbefoundinrowsoflearningdata.

Thepredictorinthispaperisonlyeffective in certainty conditionsasdescribedinthe following paragraph. The prediction is calculated in heating mode and cooling mode separately; outside temperature is assumed in therangeof-1.1°C to15.6°C forheatingcaseandintherangeof21.1°C to 32.2°C forcooling case.On the other hand,comforttemperature is assumed in therangeof15.6°C to21.1°C.Thesevalue rangesaretakenastheassumptiondueto thedataavailabilityinperformancedataof Climate MasterTranquility 27 model026 fullloadwhichiswater-to-airtypeascan beseen in theTable2⁴⁾,sothey can be

Table2.PerformanceandSpecificationsofClimateMasterHeatPump

wellinterpolatedtomakeheatpumpmodel.

4.ANN learning

ThedataforANN learningarebasically from demanddataandsupplydata.Inthis case,demanddatacontainbuildingheating loadandbuildingcoolingload.Andsupply datacontaingeothermalheatpumpelectrical load in heating mode and cooling mode. Typicalvalues for building heating load rangefrom 20to120W/m².Coolingloads generallyvaryfrom 50W/m²forbuildings incoolclimateswithlittleinternalgainsto 200W/m²ormoreforcommercialbuildings inhotclimateswithhighinternalgains⁵⁾.In thispaperbuildingheatingloadisassumed between 27 W/m²and 110 W/m²,beside that,cooling load isassumed between 50 W/m²and130W/m².Itsgraphcanbeseen inFig5.

4)GroundSourceHeatPumpClimateMasterTranquility27Manual, p.61

5)http://www.iklimnet.com/expert_hvac/cooling_load.html

Fig.5Buildingdemandloadvsoutsidetemperature

Fig.6Buildingdemandloadvs.outsidetemperature withresolvedinconflictingzone

Thedemanddataabovecanberesolved in conflicting zone between heating and cooling load demand.To resolve it,the GHP project model assumes that both equationfallto0intheconflicting region resultingthegraphbelow Fig.6.

Therefore,the outside temperature data between15.6°C and21.1°C arenotusedin neuralnetwork calculation.The GHP is assumed not operated in that range of temperature.Togettheinformationabout theheating capacity,cooling capacity,and electricityloadthataresuppliedandneeded by geothermalheatpump,itcan usethe table of Climate Master Tranquility 27 model026fullload which isprovided by manufacturer.Itisthefunctionofentering watertemperature as input.There is no enteringwatertemperaturedata,sotheycan begotbythegraphbelow,asdescribedin Fig. 7 taken from RETScreen, GSHP projectanalysis.Fig.7canalsobewritten ontheequationform asmentionedbelow :

_ _{m i n} _{} _{ }

_{m ax} _{m i n}

_{} _{ } (7)

In this paper,Tmin,Tewt,min in winter, Tewt,min in summer, Tewt,max in winter, Tewt,max in summer are assumed as 0, -1.1°C,21.1°C,15.6°C,32.2°C respectively.

To comply the heating and cooling demandasFig.7describes,theheatpump needs the electricalsupply.The value of electricalsupply depends on the value of heatingandcoolingthatwillbesuppliedby Fig.8.Itsvaluecanalsobefoundbyusing table of Climate Master Tranquility 27 model026fullload,resulting:

Fig.7Theenteringwatertemperaturevs.outside temperature

Fig.8Electricalsupplyinheatingandcoolingmode

Afterknowing alldata,eitherinputsor outputs,theANN canbetrainedseparately inheating caseandcooling casebyusing LabVIEW. Heating mode is assumed happenedalong 5months,from November until March, cooling mode is happened along 4 months,from July untilOctober, and resolved conflicting zoneishappened along 3 months,from ApriluntilJune. Training for heating mode uses 3,600 epochs and for cooling mode uses 2,880 epochs.Besidethat,toknow thevalidityof the ANN predictor, it uses root mean squareerror(RMSE):

 ^   ^



  ′^ (8)