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基于残差自回归与Kalman滤波的天然气消费量组合预测研究（英文）.docx

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基于残差自回归与Kalman滤波的天然气消费量组合预测研究（英文） ResearchonNaturalGasConsumptionCombiningResidualAutoregressionandKalmanFiltering Abstract: Forecastingnaturalgasconsumptionaccuratelyplaysacrucialroleinenergymanagementanddecision-making.Thispaperproposesacombinedforecastingapproachthatintegratesresidualautoregression(RAR)andKalmanfilteringtopredictnaturalgasconsumption.TheRARmodelisemployedtocapturethenonlinearpatternsofhistoricalconsumptiondata,whiletheKalmanfilterisutilizedtoincorporatereal-timeobservationsandupdatetheforecastedvalues.Theexperimentalresultsdemonstratethattheproposedapproachoutperformstraditionalforecastingmethodsintermsofaccuracyandstability.Thefindingsprovidevaluableinsightsforenergymanagersandpolicy-makersinoptimizingnaturalgasconsumption. 1.Introduction Theaccuratepredictionofnaturalgasconsumptionisessentialforeffectiveenergymanagementandpolicy-making.Understandingthetrendsandpatternsingasconsumptionassistsinoptimizingsupplyanddemandmanagement,pricingstrategies,andinfrastructureplanning.Traditionalforecastingmethods,suchastimeseriesanalysisandregressionmodels,havelimitationsincapturingthecomplexnonlinearitiesanddynamicspresentinnaturalgasconsumptiondata,especiallyinthepresenceofexternalshocks. 2.RelatedWork Numerousstudieshaveappliedvariousforecastingtechniquestopredictnaturalgasconsumption.Timeseriesmodels,includingautoregressiveintegratedmovingaverage(ARIMA)andseasonaldecompositionoftimeseries(STL),havebeenwidelyused.However,thesemodelsoftenfailtocapturethecomplexitiesofgasconsumptiondynamics,leadingtoinaccurateforecasts.Otherapproaches,suchasartificialneuralnetworks(ANN)andsupportvectormachines(SVM),attempttoovercometheselimitationsbuthavedifficultiesinexplainingtheunderlyingprocesses. 3.Methodology Thisresearchproposesacombinedapproachthatintegratesresidualautoregression(RAR)andKalmanfiltering.TheRARmodelcapturesthenonlinearpatternsofhistoricalgasconsumptionbymodelingtheresidualsofanautoregressiveprocess.TheresidualsarethenusedasinputstotheKalmanfilter,whi