methodsasssd

asssd  时间:2021-01-16  阅读:()
PredictionofElectricLoadNeuralNetworkPredictionModelforBigDataGuochenJin1,*,XiangyingTang2,DepingMiao21Departmentofxxxx,yyyyUniversity,Beijing,China2Schoolofaaaa,bbbbUniversity,Changsha,China*Correspondingauthor:cccc@dddd.
comKeywords:NeuralNetwork,PredictionModel,BigData.
Abstract:Powerloadforecastingisveryimportantforpowerdispatching.
Accurateloadforecastingisofgreatsignificanceforsavingenergy,reducinggeneratingcostandimprovingsocialandeconomicbenefits.
Inordertoaccuratelypredictthepowerload,basedonBPneuralnetworktheory,combinedwiththeadvantagesofClementineindealingwithbigdataandpreventingoverfitting,aneuralnetworkpredictionmodelforlargedataisconstructed.
IntroductionTheaccuratepredictionofpowerloadisofgreatsignificancefortheelectricpowerproductionandthesafeoperationofthepowergridandthenationaleconomy[1].
Shorttermloadforecastingisanimportantpartofenergymanagementsystem.
Thepredictionerrordirectlyaffectstheanalysisresultsofsubsequentsafetycheckofpowergrid,whichisofgreatsignificancefordynamicstateestimation,loadschedulingandcostreduction[2-4].
Traditionalpredictionmethodsarebasedonlinearregression,suchastimeseriesmethod,analysismethodandpatternrecognitionmethodhasdefectsofrespectively[5].
ThebasicfunamentalofBPneuralnetwork2.
1ThestructureofBPneuralnetworkBPneuralnetworkisamulti-layernetworkwitherrorreversepropagation,whichiscomposedofinputlayernodes,hiddenlayernodesandoutputlayernodes.
Thisprocesshasbeenreducedtoanacceptableleveloferrortothenetworkoutput,ortoapredeterminednumberoflearningtimes.
ThenetworkstructureisshowninFigure1.
Figure1.
NeuralnetworkstructureThegeneralmodelofartificialneuralnetworkconsistsoffourbasicelements,whichare:(1)TheBPneuralnetworkislinkedbydifferentnodecoefficients.
Whenconnectingweightsandweightsarepositive,itindicatesthatthecurrentlinkisanexcitingstate.
Conversely,ifthelinkcoefficientisnegative,thelinkstateisastateofsuppression.
(2)Theinputsignalandthelinearsignalarethecombinationofthesignalsforeachinputsignal.
(3)Thefunctionofthenonlinearactivationfunction:makingtheneuronoutputsignalwithinacertainrange.
(1)(2)(3)BPneuralnetworkisbackpropagating,mainlycomposedofthreeparts:inputlayer,middlelayerandoutputlayer.
Thenumberofnodesintheinputandoutputlayersisrelativelyeasytodetermine,butthedeterminationofthenumberofnodesinthehiddenlayerisaveryimportantandcomplexproblem.
2.
2ThedeterminationofthenumberofnetworklayersBPneuralnetworkisbackpropagating,mainlycomposedofthreeparts:inputlayer,middlelayerandoutputlayer.
Thenumberofnodesintheinputandoutputlayersisrelativelyeasytodetermine,butthedeterminationofthenumberofnodesinthehiddenlayerisaveryimportantandcomplexproblem.
Results3.
1TheestablishmentofsimulationmodelThelargedatapredictionmodelfortheuser'selectricityconsumptionisimplementedintheClementinesoftware.
3.
2AnalysisofexperimentalresultsByselectingtheloadpredictionresultsof403and411lines.
Wecanseethattheactualvaluesofthelinesbasicallymatchthepredictedvalues,buttherearealsosomeerrors,especiallyinthepeakperiodofelectricityconsumption,asshowninTable.
1.
Table.
1.
Comparisonofpowerloadforecastingof403lineComparisonPowerForecastingA1293792387B92873529837C89452323894Fromthecomparisonbetweenpredictiondataandactualdata,theBPneuralnetworkhasbetterpredictionperformanceandrelativelysmallerror,whichcanmeetthedemandcompletely,andhasfastpredictionspeedandconvenientoperation.
ConclusionsThetrendofmassdatainpowersystemprovidesabasisforloadcharacteristicanalysisandpredictionmodelestablishment,buttheclassicalloadforecastingmethodcannotaffordsuchahugetimeandcomputingresourceconsumption.
Theproblemofoverfittinginlargesamplesetwillaffectthepredictionaccuracy.
Inthispaper,apowerloadforecastingmodelisbuiltbyusingtheBPneuralnetworkmodel,makingfulluseofthepowerfuldataprocessingfunctionofClementineandpreventingtheoverfittingfunction.
TheexperimentalresultsshowthattheBPneuralnetworkmodelhasgoodpredictabilityandrobustness,andhasacertainpracticalapplicationvalue.
AcknowledgementsTheauthorsgratefullyacknowledgethefinancialsupportfromxxxfunds.
ReferencesChengQiyun,SunCaixin,ZhangXiaoxing,etal.
Short-Termloadforecastingmodelandmethodforpowersystembasedoncomplementationofneuralnetworkandfuzzylogic[J].
TransactionsofChinaElectrotechnicalSociety,2004,19(10):53-58.
Fangfang.
ResearchonpowerloadforecastingbasedonImprovedBPneuralnetwork[D].
HarbinInstituteofTechnology,2011.
AmjadyN.
Short-termhourlyloadforecastingusingtimeseriesmodelingwithpeakloadestimationcapability[J].
IEEETransactionsonPowerSystems,2001,16(4):798-805.
MaKunlong.
Shorttermdistributedloadforecastingmethodbasedonbigdata[D].
Changsha:HunanUniversity,2014.
SHIBiao,LIYuXia,YUXhua,YANWang.
Short-termloadforecastingbasedonmodifiedparticleswarmoptimizerandfuzzyneuralnetworkmodel[J].
SystemsEngineering-TheoryandPractice,2010,30(1):158-160.

Raksmart VPS主机如何设置取消自动续费

今天有看到Raksmart账户中有一台VPS主机即将到期,这台机器之前是用来测试评测使用的。这里有不打算续费,这不面对万一导致被自动续费忘记,所以我还是取消自动续费设置。如果我们也有类似的问题,这里就演示截图设置Raksmart取消自动续费。这里我们可以看到上图,在对应VPS主机的【其余操作】中可以看到默认已经是不自动续费,所以我们也不要担心被自动续费的。当然,如果有被自动续费,我们确实不想续费的...

iHostART:罗马尼亚VPS/无视DMCA抗投诉vps;2核4G/40GB SSD/100M端口月流量2TB,€20/年

ihostart怎么样?ihostart是一家国外新商家,主要提供cPanel主机、KVM VPS、大硬盘存储VPS和独立服务器,数据中心位于罗马尼亚,官方明确说明无视DMCA,对版权内容较为宽松。有需要的可以关注一下。目前,iHostART给出了罗马尼亚vps的优惠信息,罗马尼亚VPS无视DMCA、抗投诉vps/2核4G内存/40GB SSD/100M端口月流量2TB,€20/年。点击直达:ih...

bgpto:BGP促销,日本日本服务器6.5折$93/月低至6.5折、$93/月

bgpto怎么样?bgp.to日本机房、新加坡机房的独立服务器在搞特价促销,日本独立服务器低至6.5折优惠,新加坡独立服务器低至7.5折优惠,所有优惠都是循环的,终身不涨价。服务器不限制流量,支持升级带宽,免费支持Linux和Windows server中文版(还包括Windows 10). 特色:自动部署,无需人工干预,用户可以在后台自己重装系统、重启、关机等操作!bgpto主打日本(东京、大阪...

asssd为你推荐
虚拟空间租用我在网上租用了个虚拟空间。带域名。。想在里面放置AVI,等视频。放在目录里,然后怎么设置才能在域名观看呢域名价格域名费用大概是多少?美国虚拟空间国内虚拟空间与美国虚拟主机有什么不一样域名空间代理我想做域名空间代理!域名空间请问域名和空间有什么分别海外主机那些韩国主机,美国主机是怎么来的?英文域名中文域名与英文域名区别查询ip怎么查看IP地址100m网站空间100M网站空间可以存多少张图片和多少文字?免费网站空间申请哪里有免费申请空间的(网页制作)
新网域名解析 老域名全部失效请记好新域名 免费动态域名 vultr美国与日本 分销主机 免费cdn加速 godaddy域名转出 mysql主机 中国特价网 e蜗牛 元旦促销 web服务器安全 国外ip加速器 web服务器是什么 中国电信测速器 linode支付宝 阿里云个人邮箱 服务器托管价格 hdroad 火山互联 更多