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）-豆柴文库

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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