电力系统短期负荷预测毕业设计

设计（论文）内容及要求一、设计内容1了解EMS系统相关知识2确定预测目标、搜集与整理资料3对电力系统短期负荷预测进行较为系统的研究4分析资料，选择预测方法5确定短期负荷预测方法6建立短期负荷预测模型7对短期负荷预测进行仿真实验研究8进行预测分析二、设计要求1翻译该课题相关英文论文一篇2设计说明书一份（含中英文摘要、正文、程序清单）三、参考资料1能量管理系统2电力系统自动化等有关电力系统负荷预测方面的参考文献3有关MATLAB/SIMULINK仿真方面的教材及资料4神经网络技术5智能控制理论6电力系统短期负荷预测指导教师年月日本科生毕业设计论文开题报告设计论文题目基于灰色理论的短期电力负荷预测设计论文题目来源自选题目设计论文题目类型理论设计起止时间20071213200861一、设计论文依据及研究意义依据A1A0A2A3A4A5A6A7A8A9A1A0A10A11A12A13A14A15A16A18A19A17A20A21A22A23A24A25A12A15A16A26A27A28A29A30A31A33A12A32A34A36A2A3A4A5A35A37A38A39A40A41A42A30A43A45A38A44A46A47A49A1A48A50A15A51A1A0A52A53A46A47A54A55A57A18A37A56A58A59A39A40A41A60A61A2A3A4A5A62A63A64A65A56A66A67A58A36A69A68A18A2A3A4A5A12A70A71A51A37A60A73A39A12A72A74A70A71A75A76A71A18A78A77A79A1A0A80A11A56A58A81A82A75A83A84A12A85A86A87A27A67A88A89A12A90A91A36意义准确的负荷预测，可以避免经济合理的安排电网内部发电机组的启停，保持电网运行的安全稳定性，减少不必要的旋转储备容量，合理安排机组检修计划，保证社会的正常生产和生活，有效地降低发电成本，提高经济效益和社会效益。二、设计论文主要研究的内容、预期目标技术方案、路线内容1、进行系统分析2、建立灰色系统模型GM模型即灰色模型（GREYMODEL），一般来说，建模是用原始的数据序列建立差分方程；灰色系统建模则是用原始数据序列作生成数G2530建立G5506分方程。G11013于系统G15999G3134G19911G8757G7591G2530，G6164以原始数据序列G2588G10628G1998G12175G1093的G5785G1929，G17837G12193G12175G1093的数列G1075是一G12193灰色数列，G6122G13785灰色G17819程，G4557灰色G17819程建立模型，G1427成G1038灰色模型。3、运用灰色理论进行负荷预测灰色系统理论G11752G12362的是G17151G1461G5699G991建模,提G1391G1114G17151G1461G5699G991G16311G1927系统G19394题的G7044G17896G5464G4439G6238一G2011G19555机G17819程G11487作是G3324一定G14551G3272内G2476G2282的,是G994G7114G19400有G1863的灰色G17819程G4557灰色量不是G1186统计G16280G5471的G16294G5242G5224用G3835G7691本进行G11752G12362,G13792是G18331用数据生成的方G8873,G4570G7446G1093G7092G12468的原始数据G6984理成G16280G5471性G5390的生成序列G1889作G11752G123624、预测G16835差分析G3324G4557G16809G12651G13479G7536进行统计分析G1025发G10628预测G7097负荷G1552G7114，G3926G7536G2528G7114G1998G10628G8680G1517G9213G5242G12373G2476的G5785G1929，预测准确G10587G1075会G991降。G4557G8504，G6117G1216G1927定G7693据G8680G1517G9213G5242的G12373G2476程G5242分G1998G1972G1022不G2528的G16855G6984G7447G18337，G9213G5242以G21G27G263G1038分G11040，低于G21G27G263G8611G11468差G23G263G1038一G7735，高于G21G27G263G8611G11468差G21G263G1038一G7735。预期目G7643200712G4448成G13775G1680520083G6922G19610G17176G702120085建模20086G13546程三、设计论文的研究重点及难点G18337G9869建立灰色模型G2462G1866G6925进模型G19602G9869灰色模型的数G4410建模G2462G1866MATLAB程序的G13546G1901四、设计论文研究方法及步骤进度安排设计G11752G12362方G8873以定性分析G1038G1039G8505G206001、确定负荷预测目的，G2058G16758预测计划2、G6640G4559、G6984理、分析G17176G70213、建立预测模型、运用MATLABG17731件G13546程G2462仿真4、确定预测G13479G7536，分析G16835差5、G13546G1901预测报告五、进行设计论文所需条件1电力系统短期负荷预测G7691本数据（某市2003年11月电力负荷实际数据、该市2003年11月天G8680G5785G1929的数据）有G1863负荷预测和灰色理论的期刊和书籍、MATLABG17731件六、指导教师意见签名年月G7097LOADFORECASTINGEUGENEAFEINBERGSTATEUNIVERSITYOFNEWYORK,STONYBROOKEUGENEFEINBERGSUNYSBEDUDORAGENETHLIOUSTATEUNIVERSITYOFNEWYORK,STONYBROOKDGENETHLAMSSUNYSBEDUABSTRACTLOADFORECASTINGISVITALLYIMPORTANTFORTHEELECTRICINDUSTRYINTHEDEREGULATEDECONOMYITHASMANYAPPLICATIONSINCLUDINGENERGYPURCHASINGANDGENERATION,LOADSWITCHING,CONTRACTEVALUATION,ANDINFRASTRUCTUREDEVELOPMENTALARGEVARIETYOFMATHEMATICALMETHODSHAVEBEENDEVELOPEDFORLOADFORECASTINGINTHISCHAPTERWEDISCUSSVARIOUSAPPROACHESTOLOADFORECASTINGKEYWORDSLOAD,FORECASTING,STATISTICS,REGRESSION,ARTIFICIALINTELLIGENCE1INTRODUCTIONACCURATEMODELSFORELECTRICPOWERLOADFORECASTINGAREESSENTIALTOTHEOPERATIONANDPLANNINGOFAUTILITYCOMPANYLOADFORECASTINGHELPSANELECTRICUTILITYTOMAKEIMPORTANTDECISIONSINCLUDINGDECISIONSONPURCHASINGANDGENERATINGELECTRICPOWER,LOADSWITCHING,ANDINFRASTRUCTUREDEVELOPMENTLOADFORECASTSAREEXTREMELYIMPORTANTFORENERGYSUPPLIERS,ISOS,FINANCIALINSTITUTIONS,ANDOTHERPARTICIPANTSINELECTRICENERGYGENERATION,TRANSMISSION,DISTRIBUTION,ANDMARKETSLOADFORECASTSCANBEDIVIDEDINTOTHREECATEGORIESSHORTTERMFORECASTSWHICHAREUSUALLYFROMONEHOURTOONEWEEK,MEDIUMFORECASTSWHICHAREUSUALLYFROMAWEEKTOAYEAR,ANDLONGTERMFORECASTSWHICHARELONGERTHANAYEARTHEFORECASTSFORDIFFERENTTIMEHORIZONSAREIMPORTANTFORDIFFERENTOPERATIONSWITHINAUTILITYCOMPANYTHENATURESOFTHESEFORECASTSAREDIFFERENTASWELLFOREXAMPLE,FORAPARTICULARREGION,ITISPOSSIBLETOPREDICTTHENEXTDAYLOADWITHANACCURACYOFAPPROXIMATELY13HOWEVER,ITISIMPOSSIBLETOPREDICTTHENEXTYEARPEAKLOADWITHTHESIMILARACCURACYSINCEACCURATELONGTERMWEATHERFORECASTSARENOTAVAILABLEFORTHENEXTYEARPEAKFORECAST,ITISPOSSIBLETOPROVIDETHEPROBABILITYDISTRIBUTIONOFTHELOADBASEDONHISTORICALWEATHEROBSERVATIONSITISALSOPOSSIBLE,ACCORDINGTOTHEINDUSTRYPRACTICE,TOPREDICTTHESOCALLEDWEATHERNORMALIZEDLOAD,WHICHWOULDTAKEPLACEFORAVERAGEANNUALPEAKWEATHERCONDITIONSORWORSETHANAVERAGEPEAKWEATHERCONDITIONSFORAGIVENAREAWEATHERNORMALIZEDLOADISTHELOADCALCULATEDFORTHESOCALLEDNORMALWEATHERCONDITIONSWHICHARETHEAVERAGEOFTHEWEATHERCHARACTERISTICSFORTHEPEAKHISTORICALLOADSOVERACERTAINPERIODOFTIMETHEDURATIONOFTHISPERIODVARIESFROMONEUTILITYTOANOTHERMOSTCOMPANIESTAKETHELAST2530YEARSOFDATALOADFORECASTINGHASALWAYSBEENIMPORTANTFORPLANNINGANDOPERATIONALDECISIONCONDUCTEDBYUTILITYCOMPANIESHOWEVER,WITHTHEDEREGULATIONOFTHEENERGYINDUSTRIES,LOADFORECASTINGISEVENMOREIMPORTANTWITHSUPPLYANDDEMANDFLUCTUATINGANDTHECHANGESOFWEATHERCONDITIONSANDENERGYPRICESINCREASINGBYAFACTOROFTENORMOREDURINGPEAKSITUATIONS,LOADFORECASTINGISVITALLYIMPORTANTFORUTILITIESSHORTTERMLOADFORECASTINGCANHELPTOESTIMATELOADFLOWSANDTOMAKEDECISIONSTHATCANPREVENTOVERLOADINGTIMELYIMPLEMENTATIONSOFSUCHDECISIONSLEADTOTHEIMPROVEMENTOFNETWORKRELIABILITYANDTOTHEREDUCEDOCCURRENCESOFEQUIPMENTFAILURESANDBLACKOUTSLOADFORECASTINGISALSOIMPORTANTFORCONTRACTEVALUATIONSANDEVALUATIONSOFVARIOUSSOPHISTICATEDFINANCIALPRODUCTSONENERGYPRICINGOFFEREDBYTHEMARKETINTHEDEREGULATEDECONOMY,DECISIONSONCAPITALEXPENDITURESBASEDONLONGTERMFORECASTINGAREALSOMOREIMPORTANTTHANINANONDEREGULATEDECONOMYWHENRATEINCREASESCOULDBEJUSTIFIEDBYCAPITALEXPENDITUREPROJECTSMOSTFORECASTINGMETHODSUSESTATISTICALTECHNIQUESORARTIFICIALINTELLIGENCEALGORITHMSSUCHASREGRESSION,NEURALNETWORKS,FUZZYLOGIC,ANDEXPERTSYSTEMSTWOOFTHEMETHODS,SOCALLEDENDUSEANDECONOMETRICAPPROACHAREBROADLYUSEDFORMEDIUMANDLONGTERMFORECASTINGAVARIETYOFMETHODS,WHICHINCLUDETHESOCALLEDSIMILARDAYAPPROACH,VARIOUSREGRESSIONMODELS,TIMESERIES,NEURALNETWORKS,STATISTICALLEARNINGALGORITHMS,FUZZYLOGIC,ANDEXPERTSYSTEMS,HAVEBEENDEVELOPEDFORSHORTTERMFORECASTINGASWESEE,ALARGEVARIETYOFMATHEMATICALMETHODSANDIDEASHAVEBEENUSEDFORLOADFORECASTINGTHEDEVELOPMENTANDIMPROVEMENTSOFAPPROPRIATEMATHEMATICALTOOLSWILLLEADTOTHEDEVELOPMENTOFMOREACCURATELOADFORECASTINGTECHNIQUESTHEACCURACYOFLOADFORECASTINGDEPENDSNOTONLYONTHELOADFORECASTINGTECHNIQUES,BUTALSOONTHEACCURACYOFFORECASTEDWEATHERSCENARIOSWEATHERFORECASTINGISANIMPORTANTTOPICWHICHISOUTSIDEOFTHESCOPEOFTHISCHAPTERWESIMPLYMENTIONSIGNIFICANTPROGRESSINTHEDEVELOPMENTOFCOMPUTERIZEDWEATHERFORECASTINGSYSTEMS,INCLUDINGTHEMESOSCALEMODELMM5DEVELOPEDANDSUPPORTEDBYACONSORTIUMOFUNIVERSITIES2IMPORTANTFACTORSFORFORECASTSFORSHORTTERMLOADFORECASTINGSEVERALFACTORSSHOULDBECONSIDERED,SUCHASTIMEFACTORS,WEATHERDATA,ANDPOSSIBLECUSTOMERSA92CLASSESTHEMEDIUMANDLONGTERMFORECASTSTAKEINTOACCOUNTTHEHISTORICALLOADANDWEATHERDATA,THENUMBEROFCUSTOMERSINDIFFERENTCATEGORIES,THEAPPLIANCESINTHEAREAANDTHEIRCHARACTERISTICSINCLUDINGAGE,THEECONOMICANDDEMOGRAPHICDATAANDTHEIRFORECASTS,THEAPPLIANCESALESDATA,ANDOTHERFACTORSTHETIMEFACTORSINCLUDETHETIMEOFTHEYEAR,THEDAYOFTHEWEEK,ANDTHEHOUROFTHEDAYTHEREAREIMPORTANTDIFFERENCESINLOADBETWEENWEEKDAYSANDWEEKENDSTHELOADONDIFFERENTWEEKDAYSALSOCANBEHAVEDIFFERENTLYFOREXAMPLE,MONDAYSANDFRIDAYSBEINGADJACENTTOWEEKENDS,MAYHAVESTRUCTURALLYDIFFERENTLOADSTHANTUESDAYTHROUGHTHURSDAYTHISISPARTICULARLYTRUEDURINGTHESUMMERTIMEHOLIDAYSAREMOREDIFFICULTTOFORECASTTHANNONHOLIDAYSBECAUSEOFTHEIRRELATIVEINFREQUENTOCCURRENCEWEATHERCONDITIONSINFLUENCETHELOADINFACT,FORECASTEDWEATHERPARAMETERSARETHEMOSTIMPORTANTFACTORSINSHORTTERMLOADFORECASTSVARIOUSWEATHERVARIABLESCOULDBECONSIDEREDFORLOADFORECASTINGTEMPERATUREANDHUMIDITYARETHEMOSTCOMMONLYUSEDLOADPREDICTORSANELECTRICLOADPREDICTIONSURVEYPUBLISHEDININDICATEDTHATOFTHE22RESEARCHREPORTSCONSIDERED,13MADEUSEOFTEMPERATUREONLY,3MADEUSEOFTEMPERATUREANDHUMIDITY,3UTILIZEDADDITIONALWEATHERPARAMETERS,AND3USEDONLYLOADPARAMETERSAMONGTHEWEATHERVARIABLESLISTEDABOVE,TWOCOMPOSITEWEATHERVARIABLEFUNCTIONS,THETHITEMPERATUREHUMIDITYINDEXANDWCIWINDCHILLINDEX,AREBROADLYUSEDBYUTILITYCOMPANIESTHIISAMEASUREOFSUMMERHEATDISCOMFORTANDSIMILARLYWCIISCOLDSTRESSINWINTERMOSTELECTRICUTILITIESSERVECUSTOMERSOFDIFFERENTTYPESSUCHASRESIDENTIAL,COMMERCIAL,ANDINDUSTRIALTHEELECTRICUSAGEPATTERNISDIFFERENTFORCUSTOMERSTHATBELONGTODIFFERENTCLASSESBUTISSOMEWHATALIKEFORCUSTOMERSWITHINEACHCLASSTHEREFORE,MOSTUTILITIESDISTINGUISHLOADBEHAVIORONACLASSBYCLASSBASIS3FORECASTINGMETHODSOVERTHELASTFEWDECADESANUMBEROFFORECASTINGMETHODSHAVEBEENDEVELOPEDTWOOFTHEMETHODS,SOCALLEDENDUSEANDECONOMETRICAPPROACHAREBROADLYUSEDFORMEDIUMANDLONGTERMFORECASTINGAVARIETYOFMETHODS,WHICHINCLUDETHESOCALLEDSIMILARDAYAPPROACH,VARIOUSREGRESSIONMODELS,TIMESERIES,NEURALNETWORKS,EXPERTSYSTEMS,FUZZYLOGIC,ANDSTATISTICALLEARNINGALGORITHMS,AREUSEDFORSHORTTERMFORECASTINGTHEDEVELOPMENT,IMPROVEMENTS,ANDINVESTIGATIONOFTHEAPPROPRIATEMATHEMATICALTOOLSWILLLEADTOTHEDEVELOPMENTOFMOREACCURATELOADFORECASTINGTECHNIQUESSTATISTICALAPPROACHESUSUALLYREQUIREAMATHEMATICALMODELTHATREPRESENTSLOADASFUNCTIONOFDIFFERENTFACTORSSUCHASTIME,WEATHER,ANDCUSTOMERCLASSTHETWOIMPORTANTCATEGORIESOFSUCHMATHEMATICALMODELSAREADDITIVEMODELSANDMULTIPLICATIVEMODELSTHEYDIFFERINWHETHERTHEFORECASTLOADISTHESUMADDITIVEOFANUMBEROFCOMPONENTSORTHEPRODUCTMULTIPLICATIVEOFANUMBEROFFACTORSFOREXAMPLE,CHENETALPRESENTEDANADDITIVEMODELTHATTAKESTHEFORMOFPREDICTINGLOADASTHEFUNCTIONOFFOURCOMPONENTSLLNLWLSLR,WHERELISTHETOTALLOAD,LNREPRESENTSTHEA93NORMALA94PARTOFTHELOAD,WHICHISASETOFSTANDARDIZEDLOADSHAPESFOREACHA93TYPEA94OFDAYTHATHASBEENIDENTIFIEDASOCCURRINGTHROUGHOUTTHEYEAR,LWREPRESENTSTHEWEATHERSENSITIVEPARTOFTHELOAD,LSISASPECIALEVENTCOMPONENTTHATCREATEASUBSTANTIALDEVIATIONFROMTHEUSUALLOADPATTERN,ANDLRISACOMPLETELYRANDOMTERM,THENOISECHENETALALSOSUGGESTEDELECTRICITYPRICINGASANADDITIONALTERMTHATCANBEINCLUDEDINTHEMODELNATURALLY,PRICEDECREASES/INCREASESAFFECTELECTRICITYCONSUMPTIONLARGECOSTSENSITIVEINDUSTRIALANDINSTITUTIONALLOADSCANHAVEASIGNIFICANTEFFECTONLOADSTHESTUDYIN4USEDPENNSYLVANIANEWJERSEYMARYLANDPJMSPOTPRICEDATAASITRELATEDTOONTARIOHYDROLOADASANEURALNETWORKINPUTTHEAUTHORSREPORTTHATACCURATEESTIMATESWEREACHIEVEDMOREQUICKLYWITHTHEINCLUSIONOFPRICEDATAAMULTIPLICATIVEMODELMAYBEOFTHEFORMLLNA95FWA95FSA95FR,WHERELNISTHENORMALBASELOADANDTHECORRECTIONFACTORSFW,FS,ANDFRAREPOSITIVENUMBERSTHATCANINCREASEORDECREASETHEOVERALLLOADTHESECORRECTIONSAREBASEDONCURRENTWEATHERFW,SPECIALEVENTSFS,ANDRANDOMFLUCTUATIONFRFACTORSSUCHASELECTRICITYPRICINGFPANDLOADGROWTHFGCANALSOBEINCLUDEDRAHMANPRESENTEDARULEBASEDFORECASTUSINGAMULTIPLICATIVEMODELWEATHERVARIABLESANDTHEBASELOADASSOCIATEDWITHTHEWEATHERMEASURESWEREINCLUDEDINTHEMODEL31MEDIUMANDLONGTERMLOADFORECASTINGMETHODSTHEENDUSEMODELING,ECONOMETRICMODELING,ANDTHEIRCOMBINATIONSARETHEMOSTOFTENUSEDMETHODSFORMEDIUMANDLONGTERMLOADFORECASTINGDESCRIPTIONSOFAPPLIANCESUSEDBYCUSTOMERS,THESIZESOFTHEHOUSES,THEAGEOFEQUIPMENT,TECHNOLOGYCHANGES,CUSTOMERBEHAVIOR,ANDPOPULATIONDYNAMICSAREUSUALLYINCLUDEDINTHESTATISTICALANDSIMULATIONMODELSBASEDONTHESOCALLEDENDUSEAPPROACHINADDITION,ECONOMICFACTORSSUCHASPERCAPITAINCOMES,EMPLOYMENTLEVELS,ANDELECTRICITYPRICESAREINCLUDEDINECONOMETRICMODELSTHESEMODELSAREOFTENUSEDINCOMBINATIONWITHTHEENDUSEAPPROACHLONGTERMFORECASTSINCLUDETHEFORECASTSONTHEPOPULATIONCHANGES,ECONOMICDEVELOPMENT,INDUSTRIALCONSTRUCTION,ANDTECHNOLOGYDEVELOPMENTENDUSEMODELSTHEENDUSEAPPROACHDIRECTLYESTIMATESENERGYCONSUMPTIONBYUSINGEXTENSIVEINFORMATIONONENDUSEANDENDUSERS,SUCHASAPPLIANCES,THECUSTOMERUSE,THEIRAGE,SIZESOFHOUSES,ANDSOONSTATISTICALINFORMATIONABOUTCUSTOMERSALONGWITHDYNAMICSOFCHANGEISTHEBASISFORTHEFORECASTENDUSEMODELSFOCUSONTHEVARIOUSUSESOFELECTRICITYINTHERESIDENTIAL,COMMERCIAL,ANDINDUSTRIALSECTORTHESEMODELSAREBASEDONTHEPRINCIPLETHATELECTRICITYDEMANDISDERIVEDFROMCUSTOMERA92SDEMANDFORLIGHT,COOLING,HEATING,REFRIGERATION,ETCTHUSENDUSEMODELSEXPLAINENERGYDEMANDASAFUNCTIONOFTHENUMBEROFAPPLIANCESINTHEMARKETIDEALLYTHISAPPROACHISVERYACCURATEHOWEVER,ITISSENSITIVETOTHEAMOUNTANDQUALITYOFENDUSEDATAFOREXAMPLE,INTHISMETHODTHEDISTRIBUTIONOFEQUIPMENTAGEISIMPORTANTFORPARTICULARTYPESOFAPPLIANCESENDUSEFORECASTREQUIRESLESSHISTORICALDATABUTMOREINFORMATIONABOUTCUSTOMERSANDTHEIREQUIPMENTECONOMETRICMODELSTHEECONOMETRICAPPROACHCOMBINESECONOMICTHEORYANDSTATISTICALTECHNIQUESFORFORECASTINGELECTRICITYDEMANDTHEAPPROACHESTIMATESTHERELATIONSHIPSBETWEENENERGYCONSUMPTIONDEPENDENTVARIABLESANDFACTORSINFLUENCINGCONSUMPTIONTHERELATIONSHIPSAREESTIMATEDBYTHELEASTSQUARESMETHODORTIMESERIESMETHODSONEOFTHEOPTIONSINTHISFRAMEWORKISTOAGGREGATETHEECONOMETRICAPPROACH,WHENCONSUMPTIONINDIFFERENTSECTORSRESIDENTIAL,COMMERCIAL,INDUSTRIAL,ETCISCALCULATEDASAFUNCTIONOFWEATHER,ECONOMICANDOTHERVARIABLES,ANDTHENESTIMATESAREASSEMBLEDUSINGRECENTHISTORICALDATAINTEGRATIONOFTHEECONOMETRICAPPROACHINTOTHEENDUSEAPPROACHINTRODUCESBEHAVIORALCOMPONENTSINTOTHEENDUSEEQUATIONSSTATISTICALMODELBASEDLEARNINGTHEENDUSEANDECONOMETRICMETHODSREQUIREALARGEAMOUNTOFINFORMATIONRELEVANTTOAPPLIANCES,CUSTOMERS,ECONOMICS,ETCTHEIRAPPLICATIONISCOMPLICATEDANDREQUIRESHUMANPARTICIPATIONINADDITIONSUCHINFORMATIONISOFTENNOTAVAILABLEREGARDINGPARTICULARCUSTOMERSANDAUTILITYKEEPSANDSUPPORTSAPROFILEOFANA93AVERAGEA94CUSTOMERORAVERAGECUSTOMERSFORDIFFERENTTYPEOFCUSTOMERSTHEPROBLEMARISESIFTHEUTILITYWANTSTOCONDUCTNEXTYEARFORECASTSFORSUBAREAS,WHICHAREOFTENCALLEDLOADPOCKETSINTHISCASE,THEAMOUNTOFTHEWORKTHATSHOULDBEPERFORMEDINCREASESPROPORTIONALLYWITHTHENUMBEROFLOADPOCKETSINADDITION,ENDUSEPROFILESANDECONOMETRICDATAFORDIFFERENTLOADPOCKETSARETYPICALLYDIFFERENTTHECHARACTERISTICSFORPARTICULARAREASMAYBEDIFFERENTFROMTHEAVERAGECHARACTERISTICSFORTHEUTILITYANDMAYNOTBEAVAILABLEINORDERTOSIMPLIFYTHEMEDIUMTERMFORECASTS,MAKETHEMMOREACCURATE,ANDAVOIDTHEUSEOFTHEUNAVAILABLEINFORMATION,FEINBERGETALDEVELOPEDASTATISTICALMODELTHATLEARNSTHELOADMODELPARAMETERSFROMTHEHISTORICALDATAFEINBERGETALSTUDIEDLOADDATASETSPROVIDEDBYAUTILITYCOMPANYINNORTHEASTERNUSTHEFOCUSOFTHESTUDYWASTHESUMMERDATAWECOMPAREDSEVERALLOADMODELSANDCAMETOTHECONCLUSIONTHATTHEFOLLOWINGMULTIPLICATIVEMODELISTHEMOSTACCURATELTFDT,HTA95FWTRT,WHERELTISTHEACTUALLOADATTIME,DTISTHEDAYOFTHEWEEK,HTISTHEHOUROFTHEDAY,FD,HISTHEDAILYANDHOURLYCOMPONENT,WTISTHEWEATHERDATATHATINCLUDETHETEMPERATUREANDHUMIDITY,FWISTHEWEATHERFACTOR,ANDRTISARANDOMERRORINFACT,WTISAVECTORTHATCONSISTSOFTHECURRENTANDLAGGEDWEATHERVARIABLESTHISREFLECTSTHEFACTTHATELECTRICLOADDEPENDSNOTONLYONTHECURRENTWEATHERCONDITIONSBUTALSOONTHEWEATHERDURINGTHEPREVIOUSHOURSANDDAYSINPARTICULAR,THEWELLKNOWNEFFECTOFTHESOCALLEDHEATWAVESISTHATTHEUSEOFAIRCONDITIONERSINCREASESWHENTHEHOTWEATHERCONTINUESFORSEVERALDAYSTOESTIMATETHEWEATHERFACTORFW,WEUSEDTHEREGRESSIONMODELFWA960_A96JXJ,WHEREXJAREEXPLANATORYVARIABLESWHICHARENONLINEARFUNCTIONSOFCURRENTANDPASTWEATHERPARAMETERSANDA960,A96JARETHEREGRESSIONCOEFFICIENTSTHEPARAMETERSOFTHEMODELCANBECALCULATEDITERATIVELYWESTARTWITHF1THENWEUSETHEABOVEREGRESSIONMODELTOESTIMATEFTHENWEESTIMATEF,ANDSOONTHEDESCRIBEDALGORITHMDEMONSTRATEDRAPIDCONVERGENCEONHISTORICALHOURLYLOADANDWEATHERDATAWEHAVEAPPLIEDITTOMANYAREASWITHPOPULATIONBETWEEN50,000AND250,000CUSTOMERSFIGURE121PRESENTSANEXAMPLEOFASCATTERPLOTTHATCOMPARESTHEMODELANDREALPARAMETERSFIGURE122DEMONSTRATESTHECONVERGENCEOFTHECORRELATIONBETWEENTHEACTUALLOADANDTHEMODELFORTHEITERATIONPROCESSFIGURE123DEMONSTRATESTHECONVERGENCEOFTHELINEARREGRESSIONPROCEDURESINTHEALGORITHMFIGURE121SCATTERPLOTOFTHEACTUALLOADVSTHEMODELTHESOFTWARE,THATUSESTHEDESCRIBEDMETHOD,LEARNSTHEMODELPARAMETERSANDMAKESNEXTYEARPREDICTIONSBASEDONTHEMODELLOADSFORTHELAST2530YEARSOFDATATHOUGHHISTORICALLOADSMAYNOTAVAILABLE,THESOFTWAREAPPLIESTHELASTYEARMODELSTOTHEHISTORICALWEATHERDATATOESTIMATETHENEXTYEARA92SPEAKDISTRIBUTIONFIGURE122CORRELATIONBETWEENTHEACTUALLOADANDTHEMODELFIGURE123CONVERGENCEOFTHER2FORTHEACTUALLOADVSTHEMODELTHESOFTWAREGENERATESSEVERALIMPORTANTCHARACTERISTICSFOREXAMPLE,FOREACHLOADPOCKETANDFORTHESYSTEM,ITCALCULATESAWEATHERNORMALIZATIONFACTORTHATISARATIOOFTHEPEAKLOADTOTHELOADTHATWOULDBEOBSERVEDUNDERAVERAGEPEAKCONDITIONSITALSOPRODUCESPROBABILITYDISTRIBUTIONSFORTHENEXTYEARPEAKSTHEDESCRIBEDMETHODSCANBEAPPLIEDTOBOTHMEDIUMANDLONGTERMFORECASTINGHOWEVER,THELONGTERMFORECASTSSHOULDINCORPORATEECONOMICANDPOPULATIONDYNAMICFORECASTSASINPUTPARAMETERS32SHORTTERMLOADFORECASTINGMETHODSALARGEVARIETYOFSTATISTICALANDARTIFICIALINTELLIGENCETECHNIQUESHAVEBEENDEVELOPEDFORSHORTTERMLOADFORECASTINGSIMILARDAYAPPROACHTHISAPPROACHISBASEDONSEARCHINGHISTORICALDATAFORDAYSWITHINONE,TWO,ORTHREEYEARSWITHSIMILARCHARACTERISTICSTOTHEFORECASTDAYSIMILARCHARACTERISTICSINCLUDEWEATHER,DAYOFTHEWEEK,ANDTHEDATETHELOADOFASIMILARDAYISCONSIDEREDASAFORECASTINSTEADOFASINGLESIMILARDAYLOAD,THEFORECASTCANBEALINEARCOMBINATIONORREGRESSIONPROCEDURETHATCANINCLUDESEVERALSIMILARDAYSTHETRENDCOEFFICIENTSCANBEUSEDFORSIMILARDAYSINTHEPREVIOUSYEARS4FUTURERESEARCHDIRECTIONSINTHISCHAPTERWEHAVEDISCUSSEDSEVERALSTATISTICALANDARTIFICIALINTELLIGENCETECHNIQUESTHATHAVEBEENDEVELOPEDFORSHORT,MEDIUM,ANDLONGTERMELECTRICLOADFORECASTINGSEVERALSTATISTICALMODELSANDALGORITHMSTHATHAVEBEENDEVELOPEDTHOUGH,AREOPERATINGADHOCTHEACCURACYOFTHEFORECASTSCOULDBEIMPROVED,IFONEWOULDSTUDYTHESESTATISTICALMODELSANDDEVELOPMATHEMATICALTHEORYTHATEXPLAINSTHECONVERGENCEOFTHESEALGORITHMSRESEARCHERSSHOULDALSOINVESTIGATETHEBOUNDARIESOFAPPLICABILITYOFTHEDEVELOPEDMODELSANDALGORITHMSSOFAR,THEREISNOSINGLEMODELORALGORITHMTHATISSUPERIORFORALLUTILITIESTHEREASONISTHATUTILITYSERVICEAREASVARYINDIFFERINGMIXTURESOFINDUSTRIAL,COMMERCIAL,ANDRESIDENTIALCUSTOMERSTHEYALSOVARYINGEOGRAPHIC,CLIMATOLOGIC,ECONOMIC,ANDSOCIALCHARACTERISTICSSELECTINGTHEMOSTSUITABLEALGORITHMBYAUTILITYCANBEDONEBYTESTINGTHEALGORITHMSONREALDATAINFACT,SOMEUTILITYCOMPANIESUSESEVERALLOADFORECASTINGMETHODSINPARALLELASFARASWEKNOW,NOTHINGISKNOWNONAPRIORICONDITIONSTHATCOULDDETECTWHICHFORECASTINGMETHODISMORESUITABLEFORAGIVENLOADAREAANIMPORTANTQUESTIONISTOINVESTIGATETHESENSITIVITYOFTHELOADFORECASTINGALGORITHMSANDMODELSTOTHENUMBEROFCUSTOMERS,CHARACTERISTICSOFTHEAREA,ENERGYPRICES,ANDOTHERFACTORSASMENTIONEDABOVE,WEATHERISANIMPORTANTFACTORTHATINFLUENCESTHELOADTHEUSUALAPPROACHTOSHORTTERMLOADFORECASTINGUSESTHEFORECASTEDWEATHERSCENARIOASANINPUTHOWEVER,ONEOFTHEMOSTIMPORTANTRECENTDEVELOPMENTSINWEATHERFORECASTINGISTHESOCALLEDENSEMBLEAPPROACHWHICHCONSISTSOFCOMPUTINGMULTIPLEFORE