基于BP神经网络的短期负荷预测建模及其仿真研究

摘要本文主要介绍我们在人工神经网络（ArtificialNeuralNetwork）ANN在电力系统短期负荷预测中进行的研究工作。电力系统短期负荷预测的电力调度中十分重要的一个环节，其结果将对发电机生产计划的制定、水火发电的合理配置、燃料配置和安全分析设备的短期维修及电网能量的传播等产生很大的影响。在信息化建设蓬勃发展的今天，传统的人工预测已经越来越不能满足电力工业发展的需要，而以ANN为代表的预测方法则越来越显示出其巨大的优越性。准确的负荷预测有助于提高系统的安全性和经济性，能够减少发电成本。因此，寻求合适的负荷预测方法以最大限度的提高预测精度具有重要的应用价值。本文在进行短期负荷预测的过程中，主要进行以下的工作：介绍和分析国内外现有的负荷预测技术现状；简要说明ANN；几种流行BP网络算法的理论介绍和算法的选取；根据负荷组成成分的分析及主要成分的变化规律，决定神经网络的输入以及训练样本数量的选取和处理；比较不同隐含层神经元数量对预测结果的影响，然后选取最优值；检验新的预测模型在实际的使用效果；根据实际应用情况提出存在的问题和后续的研究的见解。关键词：短期负荷预测，人工神经网络，BP算法AbstractThispapermainlypresentsourworkontheresearchofshorttermloadforecastingbyusingArtificialNeuralNetwork(ANN).Theshort-termelectricloadforecastingisaveryimportantlinkintheelectricpowerdispatch,resultedinagreatimprovementintheestablishmentoftheproductionplanofgenerators,thereasonabledeploymentoffireandwatergenerateelectricityandfuel,theshort-termmaintenanceofsafeanalysisequipmentsandthespreadoftheenergyofelectricalnetwork.Nowadaysinformatizationconstructionisdevelopingsovigorouslythattraditionalartificialforecastcannotyetsatisfytheneedsofelectricpowerindustrialdevelopmentmoreandmore.However,theforecastmethodforrepresentative,whichisANN,showsitshugesuperiority.Accurateloadforecastingisadvantageoustoimproveingthesecurityandeconomiceffectofpowersystemandcanreducethecostofgeneratingelectricity.Findinganappropriateloadforecastingmethodtoimprovetheaccuracyofprecisionhasimportantapplicationgvalue.Thispapermainlyintroducedthetopicasthefollowingintheshort-termloadforecasting:Theintroduciongofandanalysisofexistingshort-termloadforecasttechnologybothdomesticandoverseas.ThebriefintroductiononANN.ThemainpopularBP(ErrorBackPropagationNetwork)trainingfunctionandthechoiceinthisfunction.Accordingtotheannlysisofcompositionoftheloadandthechangeruleofthemainfactor,decidetheinputofthearrificialneuralnetworkaswellasthechoiceandmanipulantionofthetiainingsamples.Someformulasonchoiceofthenumberofhiddenlayer,thecomparisonondifferentcodenumberofhiddenlayerandtheirinfluenceontheforecastresultafterthatgetthebestchoice.Testthenewmodulethroughasample.Theimprovementtofurtherstudyandthesolutionaccordingtotheproblemappearedinthepratice.Keywords:short-termloadforecasting,artificialneuralnetwork,BPtrainingfunction目录1绪论...........................................................................................................................11.1负荷预测的背景..............................................................................................11.2负荷预测概述.................................................................................................11.2.1负荷预测的特点...................................................................................11.2.2负荷预测的基本原理...........................................................................21.2.3负荷预测的意义...................................................................................31.3国内外负荷预测的研究现状.........................................................................42负荷预测方法综述...................................................................................................62.1传统的预测方法.............................................................................................62.1.1时间序列法...........................................................................................62.1.2回归预测法...........................................................................................72.1.3趋势外推法...........................................................................................72.2负荷预测技术的新进展.................................................................................82.2.1模糊控制法...........................................................................................82.2.2人工神经网络预测法...........................................................................82.2.3优选组合预测技术...............................................................................82.3本章小结.........................................................................................................93人工神经网络介绍...............................................................................................103.1人工神经网络综述.......................................................................................103.1.1人工神经网络的基本模型.................................................................103.1.2人工神经网络的特征.........................................................................143.1.3人工神经网络的分类.........................................................................143.1.4神经网络的发展概况.........................................................................153.2BP神经网络..................................................................................................163.2.1BP网络模型与结构............................................................................173.2.2BP神经网络的学习规则....................................................................183.2.3BP网络的局限性及改进....................................................................223.3本章小结.......................................................................................................244基于BP网络负荷预测建模及其仿真研究......