神经网络英文文献.docx

神经网络英文文献.docx

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神经网络英文文献

ARTIFICIALNEURALNETWORKFORLOADFORECASTING

INSMARTGRID

HAO-TIANZHANG,FANG-YUANXU,LONGZHOU

EnergySystemGroup,CityUniversityLondon,NorthamptonSquare,London,UKE-MAIL:

,long.zhou.

Abstract:

Itisanirresistibletrendoftheelectricpowerimprovementfordevelopingthesmartgrid,whichappliesalargeamountofnewtechnologiesinpowergeneration,transmission,distributionandutilizationtoachieveoptimizationofthepowerconfigurationandenergysaving.Asoneofthekeylinkstomakeagridsmarter,loadforecastplaysasignificantroleinplanningandoperationinpowersystem.ManywayssuchasExpertSystems,GreySystemTheory,andArtificialNeuralNetwork（ANN）andsoonareemployedintoloadforecasttodothesimulation.ThispaperintendstoillustratetherepresentationoftheANNappliedinloadforecastbasedonpracticalsituationinOntarioProvince,Canada.

Keywords:

Loadforecast;ArtificialNeuronNetwork;backpropagationtraining;Matlab

1.Introduction

Loadforecastingisvitallybeneficialtothepowersystemindustriesinmanyaspects.Asanessentialpartinthesmartgrid,highaccuracyoftheloadforecastingisrequiredtogivetheexactinformationaboutthepowerpurchasingandgenerationinelectricitymarket,preventmoreenergyfromwastingandabusingandmakingtheelectricitypriceinareasonablerangeandsoon.Factorssuchasseasondifferences,climatechanges,weekendsandholidays,disastersandpoliticalreasons,operationscenariosofthepowerplantsandfaultsoccurringonthenetworkleadtochangesoftheloaddemandandgenerations.

Since1990,theartificialneuralnetwork（ANN）hasbeenresearchedtoapplyintoforecastingtheload.“ANNsaremassivelyparallelnetworksofsimpleprocessingelementsdesignedtoemulatethefunctionsandstructureofthebraintosolveverycomplexproblems”.Owingtothe

transcendentcharacteristics,ANNsisoneofthemostcompetentmethodstodothepracticalworkslikeloadforecasting.Thispaperconcernsaboutthebehaviorsofartificialneuralnetworkinloadforecasting.AnalysisofthefactorsaffectingtheloaddemandinOntario,CanadaismadetogiveaneffectivewayforloadforecastinOntario.

2.BackPropagationNetwork

2.1.Background

Becausetheoutstandingcharacteristicofthestatisticalandmodelingcapabilities,ANNtoulddealwithnon-linearandcomplexproblemsintermsofclassificationorforecasting.Astheproblemdefined,therelationshipbetweentheinputandtargetisnon-linearandverycomplicated.ANNisanappropriatemethodtoapplyintotheproblemtoforecasttheloadsituation.Forapplyingintotheloadforecast,anANN

needstoselectanetworktypesuchasFeed-forwardBackPropagation,LayerRecurrentandFeed-forwardtime-delayandsoon.Todate,Backpropagationiswidelyusedinneuralnetworks,whichisafeed-forwardnetworkwithcontinuouslyvaluedfunctionsandsupervisedlearning.Itcanmatchtheinputdataandcorrespondingoutputinanappropriatewaytoapproachacertainfunctionwhichisusedforachievinganexpectedgoalwithsomepreviousdatainthesamemanneroftheinput.

2.2.Architectureofbackpropagationalgorithm

Figure1showsasingleNeuronmodelofbackpropagationalgorithm.

Generally,theoutputisafunctionofthesumofbiasandweightmultipliedbytheinput.Theactivationfunctioncouldbeanykindsoffunctions.However,thegeneratedoutputisdifferent.

Owingtothefeed-forwardnetwork,ingeneral,atleastonehiddenlayerbeforetheoutputlayerisneeded.Three-layernetworkisselectedasthearchitecture,becausethiskindofarchitecturecanapproximateanyfunctionwithafewdiscontinuities.ThearchitecturewiththreelayersisshowninFigure2below:

Figure1.Neuronmodelofbackpropagationalgorithm

Figure2.Architectureofthree-layerfeed-forwardnetwork

Basically,therearethreeactivationfunctionsappliedintobackpropagationalgorithm,namely,Log-Sigmoid,Tan-Sigmoid,andLinearTransferFunction.TheoutputrangeineachfunctionisillustratedinFigure3below.

Figure.3.Activationfunctionsappliedinbackpropagation（a）Log-sigmoid（b）Tan-sigmoid（c）linearfunction

2.3.Trainingfunctionselection

AlgorithmsoftrainingfunctionemployedbasedonbackpropagationapproachareusedandthefunctionwasintegratedintheMatlabNeuronnetworktoolbox.

Functionname

Algorkhtn

trainb

Batchtrainingwithweighl&biaslearningrules

irainbfg

BFGSquasi-Xewtonbackpropagation

trainbr

Bayesianlugularization

trainc

Cyclicalorderincrementaltrainingw/learningfunclions

iraincgb

Powell-Bealeconjugategradientbackpropagation

intinegf

FlctchepPowcllcoiijugaiL'pradicnthackpropagaiion

[raincgp

Polak-Ribiervconjugategradientbackpjopagatiort

traingd

Gradientdescentbackpropagaiioji

miingdm

Gradientduscentwithmoineniuinbackpropagalion

iraingda

GradienidescentwithadaptiveIrbackpropagation

iniingdx

GradientLlescentw/tnomcimun&adaptivekbackpropagaiion

train]m

Lcvenberg-Marquard【backpropagation

irainoss

Onestepsecantbackpropagaiion

trainr

RandomorderiiKreineiilaltrainingu/iearnin^funclions

irainrp

ResidentbackpropagationtRprop）

trains

Scquetuialorderinccementaltrainingw/leamingfunctions

irainscg

Scaledconjugategradientbackpropagation

TABLE」.TRAININGFUNCTIONSINMATLAB'SNNTOOLBOX

3.TrainingProcedures

3.1.Backgroundanalysis

TheneuralnetworktrainingisbasedontheloaddemandandweatherconditionsinOntarioProvince,CanadawhichislocatedinthesouthofCanada.TheregioninOntariocanbedividedintothreepartswhicharesouthwest,centralandeast,andnorth,accordingtotheweatherconditions.Thepopulationisgatheredaroundsoutheasternpartoftheentireprovince,whichincludestwoofthelargestcitiesofCanada,TorontoandOttawa.

3.2.DataAcquisition

Therequiredtrainingdatacanbedividedintotwoparts:

inputvectorsandoutputtargets.Forloadforecasting,inputvectorsfortrainingincludealltheinformationoffactorsaffectingtheloaddemandchange,suchasweatherinformation,holidaysorworkingdays,faultoccurringinthenetworkandsoon.Outputtargetsaretherealtimeloadscenarios,whichmeanthedemandpresentedatthesametimeasinputvectorschanging.

Owingtotheconditionalrestriction,thisstudyonlyconsiderstheweatherinformationandlogicaladjustmentofweekdaysandweekendsasthefactorsaffectingtheloadstatus.Inthispaper,factorsaffectingtheloadchangingarelistedbelow:

（1）.Temperature（°C）

（2）.DewPointTemperature（C）

（3）.RelativeHumidity（%）

（4）.Windspeed（km/h）

（5）.WindDirection（10）

（6）.Visibility（km）

（7）.Atmosphericpressure（kPa）

（8）.Logicaladjustmentofweekdayorweekend

Accordingtotheinformationgatheredabove,theweatherinformationinTorontotakenplaceofthewholeOntarioprovinceischosentoprovidedataacquisition.Thedatawasgatheredhourlyaccordingtothehistoricalweatherconditionsremainedintheweatherstations.Loaddemanddataalsoneedstobegatheredhourlyandcorrespondingly.Inthispaper,2yearsweatherdataandloaddataiscollectedtotrainandtestthecreatednetwork.

3.3.DataNormalization

Owingtopreventthesimulatedneuronsfrombeingdriventoofarintosaturation,allofthegathereddataneedstobenormalizedafteracquisition.Likeperunitsystem,eachinputandtargetdataarerequiredtobedividedbythemaximumabsolutevalueincorrespondingfactor.Eachvalueofthenormalizeddataiswithintherangebetween-1and+1sothattheANNcouldrecognizethedataeasily.Besides,weekdaysarerepresentedas1,andweekendarerepresentedas0.

3.4.Neuralnetworkcreating

ToolboxinMatlabisusedfortrainingandsimulatingtheneuronnetwork.Thelayoutoftheneuralnetworkconsistsofnumberofneuronsandlayers,connectivityoflayers,activationfunctions,anderrorgoalandsoon.Itdependsonthepracticalsituationtosettheframeworkandparametersofthenetwork.ThearchitectureoftheANNcouldbeselectedtoachievetheoptimizedresult.Matlabisoneofthebestsimulationtoolstoprovidevisiblewindows.Three-layerarchitecturehasbeenchosentogivethesimulationasshowninFigure2above.Itisadequatetoapproximatearbitraryfunction,ifthenodesofthehiddenlayeraresufficient.

Duetothepracticalinputvalueisfrom-1to+1,thetransferfunctionofthefirstlayerissettobetansigmiod,whichisahyperbolictangentsigmoidtransferfunction.Thetransferfunctionoftheoutputlayerissettobelinearfunction,whichisalinearfunctiontocalculatealayer'soutputfromitsnetinput.Thereisoneadvantageforthelinearoutputtransferfunction:

becausethelinearoutputneuronsleadtotheoutputtakeonanyvalue,thereisnodifficultytofindoutthedifferencesbetweenoutputandtarget.

Thenextstepistheneuronsandtrainingfunctionsselection.

Generally,TrainbrandTrainlmarethebestchoicesaroundallofthetrainingfunctionsinMatlabtoolbox

Trainlm（Levenberg-Marquardtalgorithm）isthefastesttrainingalgorithmfornetworkswithmoderatesize.However,thebigproblem

appearsthatitneedsthestorageofsomematriceswhichissometimeslargefortheproblems.Whenthetrainingsetislarge,trainlmalgorithmwillreducethememoryandalwayscomputetheapproximateHessianmatrixwithnxndimensions.AnotherdrawbackofthetrainImisthattheover-fittingwilloccurwhenthenumberoftheneuronsistoolarge.Basically,thenumberofneuronsisnottooIargewhenthetrainImaIgorithmisempIoyedintothenetwork.Trainbr（Ba