Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting

Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting

ID:39715820

大小:1.22 MB

页数:8页

时间:2019-07-09

Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting_第1页
Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting_第2页
Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting_第3页
Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting_第4页
Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting_第5页
资源描述:

《Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库

1、1266IEEETRANSACTIONSONPOWERSYSTEMS,VOL.25,NO.3,AUGUST2010IntelligentHybridWaveletModelsforShort-TermLoadForecastingAjayShekharPandey,DevenderSingh,andSunilKumarSinhaAbstract—Awaveletdecompositionbasedloadforecastap-Theelectricalloadatanyparticulartimeisusuallyassumedproachisproposedfor24-ha

2、nd168-haheadshort-termloadtobealinearcombinationofdifferentcomponents.Fromtheforecasting.Theproposedapproachisappliedtoandcomparedsignalanalysispointofview,loadcanalsobeconsideredasawithrepresentativeloadforecastingmethodssuchas:timese-linearcombinationofdifferentfrequencies.Thewavelettrans

3、-riesintraditionalapproachesandRBFneuralnetworkandformisintroducedtopreprocesstheloaddatainordertoen-neuro-fuzzyforecasterinnontraditionalapproaches.Theotherforecasters,suchasmultiplelinearregression(MLR),timeseries,hancetheaccuracyofforecasting.Inthiswork,inadditiontofeedforwardneuralnetwo

4、rk(FFNN),radialbasisfunctionneuralthehistoricalelectricitydemanddatathetemperature,whichhasnetwork(RBFNN),clustering,andfuzzyinferenceneuralnetworksignificantimpactonenergyconsumption,havealsobeenused.(FINN),reportedintheliteraturearealsocomparedwiththeTheloaddataaretransformedinlowandhighfr

5、equencycom-presentapproach.Theprocessoftheproposedwaveletdecompo-ponents.Itisshownthatthehighfrequencycomponentdoesnotsitionapproachisthatitfirstdecomposesthehistoricalloadandchangefromareferencedaytotheforecastdaywhereasthelowweathervariablesintoanapproximatepartassociatedwithlowfrequencies

6、andseveraldetailpartsassociatedwithhighfrequen-frequencycomponentissituation(climate)dependant.Henceaciescomponentsthroughthewavelettransform.Thehistoricalforecastmodelisdevelopedforthelowfrequencycomponentdataaresmoothenedbydeletingthehighfrequencycomponentsonly.andfedasinputtotheproposedm

7、odelsfortheprediction.Acom-AvarietyoftechniquesforSTLFbasedonconventionalsta-parisonofwaveletandnon-waveletbasedapproachesshowsthetisticalmethods,ANNbasedmodels,expertsystemsandhybridsuperiorityofproposedwaveletbasedapproachovernon-waveletmodelhavebeenpr

当前文档最多预览五页,下载文档查看全文

此文档下载收益归作者所有

当前文档最多预览五页,下载文档查看全文
温馨提示:
1. 部分包含数学公式或PPT动画的文件,查看预览时可能会显示错乱或异常,文件下载后无此问题,请放心下载。
2. 本文档由用户上传,版权归属用户,天天文库负责整理代发布。如果您对本文档版权有争议请及时联系客服。
3. 下载前请仔细阅读文档内容,确认文档内容符合您的需求后进行下载,若出现内容与标题不符可向本站投诉处理。
4. 下载文档时可能由于网络波动等原因无法下载或下载错误,付费完成后未能成功下载的用户请联系客服处理。