基于NARX神经网络的短期多步太阳辐射预测的混合分解强化模型（英文）-豆柴文库

您所在位置：网站首页 / 基于NARX神经网络的短期多步太阳辐射预测的混合分解强化模型（英文）.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

基于NARX神经网络的短期多步太阳辐射预测的混合分解强化模型（英文）
Title:HybridDecompositionReinforcementModelforShort-TermMulti-StepSolarRadiationPredictionBasedonNARXNeuralNetwork
Abstract:
Solarradiationpredictionplaysavitalroleinvariousfieldssuchasrenewableenergymanagement,weatherforecasting,andsmartgridoperations.Accurateshort-termmulti-stepsolarradiationpredictionischallengingduetothecomplexandnonlinearnatureofsolarradiationdynamics.Inthispaper,weproposeanovelhybriddecompositionreinforcementmodelbasedontheNonlinearAutoregressiveExogenous(NARX)neuralnetworktoimprovetheaccuracyofshort-termmulti-stepsolarradiationprediction.
1.Introduction:
Solarradiationisacrucialfactorintheestimationofsolarenergygenerationandtheoptimizationofitsutilization.Accuratesolarradiationpredictionisnecessaryforefficientgridmanagement,solarpowerplants,andweatherforecastingsystems.However,duetothevariabilityandnonlinearityofsolarradiation,accuratepredictionremainsachallengingtask.ThispaperaimstoaddressthischallengethroughahybriddecompositionreinforcementmodelthatcombinesthestrengthsofNARXneuralnetworksanddecompositiontechniques.
2.RelatedWork:
Severalpredictionmodelshavebeendevelopedforsolarradiationprediction,includingphysicalmodels,statisticalmodels,andmachinelearningmodels.Amongthese,neuralnetworkshaveshownpromisingresults.TheNARXneuralnetworkhasbeenusedsuccessfullyinvarioustimeseriespredictiontasks,includingsolarradiationprediction.However,traditionalNARXmodelssufferfromlimitationssuchasinherenthigh-dimensionalinputandnoisydata.Toovercometheselimitations,decompositiontechniquescanbeappliedtotheinputvariablesbeforefeedingthemintotheNARXmodel.
3.Methodology:
Theproposedhybriddecompositionreinforcementmodelconsistsofthreemainsteps:decomposition,NARXneuralnetwork,andreinforcementlearning.Firstly,theinputdata,includingmeteorologicalvariablesandhistoricalsolarradiationdata,aredecomposedintoseveralsubseriesusingasuitabledecompositiontechnique,suchasSingularSpectrumAnalysis(SSA)orEmpiricalModeDecomposition(EMD).Thedecompose