Matlab regression prediction of slime mold optimized bidirectional long short-term memory network (SMA-BILSTM)

?Abouttheauthor:AMatlabsimulationdeveloperwholovesscientificresearch.Hecultivateshismindandimproveshistechnologysimultaneously.ForcooperationonMATLABprojects,pleasesendaprivatemessage.

Personalhomepage:MatlabResearchStudio

Personalcredo:Investigatethingstogainknowledge.

Withtherapiddevelopmentofrenewableenergy,windpowergeneration,asanimportantpartofit,hasreceivedwidespreadattention.However,duetotheuncertaintyandvolatilityofwindpowergeneration,accuratepredictionofwindpowergenerationhasbecomeanimportantissue.Inordertosolvethisproblem,manyresearchershavebeguntoexploretheuseofmachinelearningalgorithmsforwindpowerdataprediction.

Amongmachinelearningalgorithms,longshort-termmemorynetwork(LSTM)isacommonlyusedrecurrentneuralnetwork(RNN)modelthatperformswellinprocessingsequencedata.LSTMnetworksareabletocapturelong-termdependenciesinsequencedata,therebyimprovingpredictionaccuracy.However,thetraditionalLSTMmodeloftenhassomeproblemswhenprocessingwindpowerdata,suchasslowmodeltrainingspeedandlowpredictionaccuracy.

Inordertosolvetheseproblems,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheSlimeMoldAlgorithm(SMA).Theslimemoldalgorithmisaheuristicalgorithmthatsimulatesthebehaviorofslimemoldsintheprocessofsearchingforfood.Ithasthecharacteristicsofglobalsearchandadaptability.ByapplyingtheslimemoldalgorithmtotheoptimizationprocessoftheLSTMmodel,thetrainingspeedandpredictionaccuracyofthemodelcanbeeffectivelyimproved.

Intheexperiment,weusedasetofwindpowerdatasetstotrainandtestthemodel.First,wepreprocessedtheoriginalwindpowerdata,includingdatacleaning,featureextraction,etc.Then,weusedtheslimemoldalgorithmtooptimizetheparametersoftheSMA-biLSTMmodel.Finally,wecomparedtheresultsofwindpowerdatapredictionusingthetraditionalLSTMmodelandtheoptimizedSMA-biLSTMmodel.

ExperimentalresultsshowthattheoptimizedSMA-biLSTMmodelshowsobviousadvantagesinwindpowerdataprediction.ComparedwiththetraditionalLSTMmodel,theSMA-biLSTMmodelhashigherpredictionaccuracyandfastertrainingspeed.ThisshowsthattheperformanceoftheLSTMmodelinwindpowerdatapredictioncanbeeffectivelyimprovedbyintroducingtheslimemoldalgorithmforoptimization.

Insummary,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheslimemoldalgorithmforwindpowerdataprediction.Experimentalresultsshowthatthemodelhasgoodperformanceinwindpowerdataprediction.Futureresearchcanfurtherexploretheapplicationofotheroptimizationalgorithmstofurtherimprovetheaccuracyandefficiencyofwindpowerdataprediction.

Corecode

functionhuatu(fitness,process,type)
figure
plot(fitness)
gridon
title([type,'Fitnesscurve'])
xlabel('Numberofiterations/times')
ylabel('Fitnessvalue/MSE')

figure
subplot(2,2,1)
plot(process(:,1))
gridon
xlabel('Numberofiterations/times')
ylabel('L1/piece')

subplot(2,2,2)
plot(process(:,2))
gridon
xlabel('Numberofiterations/times')
ylabel('L2/piece')

subplot(2,2,3)
plot(process(:,3))
gridon
xlabel('Numberofiterations/times')
ylabel('K/times')

subplot(2,2,4)
plot(process(:,4))
gridon
xlabel('Numberofiterations/times')
ylabel('lr')
subtitle([type,'Hyperparameterschangewiththenumberofiterations'])

?References

[1]LongZhongxiu.Researchandimplementationoflandslidepredictionbasedonsoilslopedataclassificationmodel[D].SouthwestJiaotongUniversity,2020.

[2]WangYongsheng.Researchonshort-termwindpoweroutputpowerpredictionbasedondeeplearning[J].[2023-09-08].

[3]WangHaiyue.Researchontheapplicationoffuzzyinferencesystembasedongranularcomputingintimeseriesdataprediction[D].ShandongNormalUniversity,2019.

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