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基于改进混沌搜索的AMPSO-BP短期负荷预测.docx

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基于改进混沌搜索的AMPSO-BP短期负荷预测 Title:ImprovedChaoticSearchBasedAMPSO-BPShort-termLoadForecasting Abstract: Short-termloadforecastingplaysacrucialroleintheeffectiveoperationofpowersystems.Accuratepredictionsoffutureelectricitydemandhelpensuregridstabilityandoptimizeenergyresourceallocation.Thispaperpresentsanimprovedapproachforshort-termloadforecastingbasedonanenhancedhybridalgorithmcalledAMPSO-BP,whichcombinesAntColonyOptimization(ACO),ModifiedParticleSwarmOptimization(MPSO),andBackpropagation(BP)neuralnetworks.Toimprovethesearchabilityofthealgorithm,achaoticsearchstrategyisintroduced.Theproposedmethodoutperformsotherexistingtechniquesbyachievinghigheraccuracyandimprovedconvergenceforshort-termloadforecasting. 1.Introduction Theaccurateforecastingofshort-termloadisessentialinelectricitydemandmanagementforpowersystemplanning,operation,anddecision-making.Short-termloadforecastinghelpstooptimizeenergyresourceallocation,reducescosts,andensuresgridstability.Inrecentyears,severalforecastingmethods,includingneuralnetworks,statisticalmodels,andoptimizationalgorithms,havebeenusedforloadforecasting.However,thesemethodshavelimitationsintermsofaccuracy,robustness,andconvergence. 2.AMPSO-BPHybridAlgorithm TheproposedforecastingapproachcombinesAntColonyOptimization,ModifiedParticleSwarmOptimization,andBackpropagationneuralnetworks(AMPSO-BP).ACOoptimizestheinitialweightsandbiasesoftheneuralnetwork,MPSOoptimizestheparametersoftheneuralnetworkfortraining,andBPisusedtofine-tunethenetwork.Thishybridalgorithmtakesadvantageofthestrengthsofeachcomponenttoenhancetheoverallperformanceofloadforecasting. 3.ChaoticSearchStrategy ToimprovethesearchabilityoftheAMPSO-BPalgorithm,achaoticsearchstrategyisintroduced.Chaoticsystemsexhibitcomplexandirregularbehaviorsthatcanenhancetheexplorationabilityofoptimizationalgorithms.ByincorporatingchaoticsearchintotheAMPSO-BPalgorithm,theglobalsearchcapabilityisimproved,leadingtomoreaccurateloadforecasts. 4.ExperimentalSetup Theproposedmethodisevaluatedusingareal-w