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1、模式识别大作业——外文翻译ArtificialNeuralNetworksinShortTermloadForecasting人工神经网络在短期负荷预测中的应用姓名：刘德龙学号：03081413班级：030814日期：2011.05外文文献原文:ArtificialNeuralNetworksinShortTermloadForecastingK.F.Reinschmidt,PresidentB.LingStonehWebsterAdvancedSystemsDevelopmentServices,Inc.245SummerStreetBoston,U02210Phone:617-
2、589-1841Abstract：Wediscusstheuseofartificialneuralnetworkstotheshorttermforecastingofloads.Inthissystem,therearetwotypesofneuralnetworks:non-linearandlinearneuralnetworks.Thenonlinearneuralnetworkisusedtocapturethehighlynon-linearrelationbetweentheloadandvariousinputparameters.Aneuralnetworkba
3、sedARMAmodelismainlyusedtocapturetheloadvariationoveraveryshorttimeperiod.Oursystemcanachieveagoodaccuracyinshorttermloadforecasting.Keywords:short-termloadforecasting,artificialneuralnetwork1、IntroductionShortterm(hourly)loadforecastingisanessentialhctioninelectricpoweroperations.Accurateshoi
4、rttermloadforecastsareessentialforefficientgenerationdispatch,unitcommitment,demandsidemanagement,shorttermmaintenanceschedulingandotherpurposes.Improvementsintheaccuracyofshorttermloadforecastscanresultinsignificantfinancialsavingsforutilitiesandcogenerators.Variousteclmiquesforpowersystemloa
5、dforecastinghavebeenreportedinliterature.Thoseinclude:multiplelinearregression,timeseries,generalexponentialsmoothing,Kalmanfiltering,expertsystem,andartificialneuralnetworks.Duetothehighlynonlinearrelationsbetweenpowerloadandvariousparameters(whethertemperature,humidity,windspeed,etc.),non-li
6、neartechniques,bothformodelingandforecasting,tendtoplaymajorrolesinthepowerloadforecasting.Theartificialneuralnetwork(A")representsoneofthosepotentialnon-lineartechniques.However,theneuralnetworksusedinloadforecastingtendtobelargeinsizeduetothecomplexityofthesystem.Therefore,trainingofsuchalar
7、genetbecomesamajorissuesincetheenduserisexpectedtorunthissystematdailyorevenhourlybasis.Inthispaper,weconsiderahybridneuralnetworkbasedloadforecastingsystem.Inthisnetwork,therearetwotypesofneuralnetworks:non-linearandlinearneuralnetwork
