基于混沌-RBF神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率超短期預(yù)測(cè)模型

基于混沌-RBF神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率超短期預(yù)測(cè)模型基于混沌-RBF神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率超短期預(yù)測(cè)模型

摘要：光伏發(fā)電作為一種清潔、可持續(xù)的能源，受到越來越多的關(guān)注。為了更好地利用光伏發(fā)電，準(zhǔn)確預(yù)測(cè)光伏發(fā)電功率對(duì)智能電網(wǎng)和能源調(diào)度具有重要意義。本文提出了一種基于混沌-RBF神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率超短期預(yù)測(cè)模型，通過引入混沌序列作為數(shù)據(jù)預(yù)處理方法，并結(jié)合RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)，提高了預(yù)測(cè)的準(zhǔn)確性和穩(wěn)定性。實(shí)驗(yàn)結(jié)果表明，該模型能夠有效地預(yù)測(cè)光伏發(fā)電功率，在超短期預(yù)測(cè)中具有較高的精度和可靠性。

1.引言

隨著全球能源需求的增長和環(huán)境保護(hù)意識(shí)的提高，光伏發(fā)電被廣泛應(yīng)用。然而，光伏發(fā)電受天氣、環(huán)境等因素的影響，具有波動(dòng)性強(qiáng)、不穩(wěn)定的特點(diǎn)。為了更好地應(yīng)對(duì)這些問題，提前進(jìn)行光伏發(fā)電功率的預(yù)測(cè)，對(duì)智能電網(wǎng)和能源調(diào)度具有重要意義。

2.研究方法

2.1混沌序列的引入

混沌序列是一種具有無規(guī)律性、非周期性和敏感依賴初值的動(dòng)態(tài)系統(tǒng)序列。通過引入混沌序列，可以將光伏發(fā)電功率數(shù)據(jù)進(jìn)行預(yù)處理，減少噪聲的影響，提取出數(shù)據(jù)的潛在規(guī)律。

2.2RBF神經(jīng)網(wǎng)絡(luò)

RBF神經(jīng)網(wǎng)絡(luò)是一種常用的人工神經(jīng)網(wǎng)絡(luò)模型。其通過采用徑向基函數(shù)作為激活函數(shù)，能夠處理非線性問題，并且具有較好的逼近能力和泛化能力。在本文中，將RBF神經(jīng)網(wǎng)絡(luò)應(yīng)用于光伏發(fā)電功率預(yù)測(cè)模型中。

3.混沌-RBF神經(jīng)網(wǎng)絡(luò)模型的構(gòu)建

3.1數(shù)據(jù)預(yù)處理

首先，收集光伏發(fā)電功率的時(shí)間序列數(shù)據(jù)。然后，利用混沌序列對(duì)原始數(shù)據(jù)進(jìn)行處理，得到預(yù)處理后的數(shù)據(jù)。通過混沌序列的引入，可以減少數(shù)據(jù)間的相關(guān)性，提取出數(shù)據(jù)的潛在規(guī)律，為后續(xù)的預(yù)測(cè)建模做準(zhǔn)備。

3.2模型訓(xùn)練與驗(yàn)證

將預(yù)處理后的數(shù)據(jù)劃分為訓(xùn)練集和驗(yàn)證集。利用訓(xùn)練集對(duì)混沌-RBF神經(jīng)網(wǎng)絡(luò)模型進(jìn)行訓(xùn)練，不斷調(diào)整模型參數(shù)，提高模型的擬合能力。然后，利用驗(yàn)證集對(duì)模型進(jìn)行驗(yàn)證，評(píng)估模型的性能。

4.實(shí)驗(yàn)與結(jié)果分析

通過實(shí)驗(yàn)對(duì)所提出的混沌-RBF神經(jīng)網(wǎng)絡(luò)模型進(jìn)行驗(yàn)證。選取實(shí)際光伏發(fā)電功率數(shù)據(jù)作為樣本，并與其他常用模型進(jìn)行比較。實(shí)驗(yàn)結(jié)果表明，所提出的模型在光伏發(fā)電功率超短期預(yù)測(cè)中具有較高的精度和可靠性。

5.總結(jié)與展望

本文提出了一種基于混沌-RBF神經(jīng)網(wǎng)絡(luò)的光伏發(fā)電功率超短期預(yù)測(cè)模型。通過引入混沌序列進(jìn)行數(shù)據(jù)預(yù)處理，并結(jié)合RBF神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)，提高了預(yù)測(cè)的準(zhǔn)確性和穩(wěn)定性。實(shí)驗(yàn)證明，該模型在光伏發(fā)電功率預(yù)測(cè)中具有較好的效果。未來的研究可以進(jìn)一步優(yōu)化模型的結(jié)構(gòu)和算法，提高預(yù)測(cè)的精度和實(shí)用性。

關(guān)鍵詞：光伏發(fā)電；功率預(yù)測(cè)；混沌序列；RBF神經(jīng)網(wǎng)絡(luò)；超短期預(yù)測(cè)。

Abstract:Photovoltaicpowergeneration,asacleanandsustainableenergysource,hasattractedincreasingattention.Inordertobetterutilizephotovoltaicpowergeneration,accuratepredictionofphotovoltaicpowerisofgreatsignificancetosmartgridandenergyscheduling.Inthispaper,aphotovoltaicpowerultra-short-termpredictionmodelbasedonchaotic-RBFneuralnetworkisproposed.ByintroducingchaoticsequencesasdatapreprocessingmethodandcombiningwithRBFneuralnetworkforprediction,theaccuracyandstabilityofpredictionareimproved.Experimentalresultsshowthattheproposedmodelcaneffectivelypredictphotovoltaicpowerandhashighaccuracyandreliabilityinultra-short-termprediction.

Introduction:Withthegrowthofglobalenergydemandandtheimprovementofenvironmentalawareness,photovoltaicpowergenerationiswidelyused.However,photovoltaicpowergenerationisinfluencedbyfactorssuchasweatherandenvironment,andhasstrongvolatilityandinstability.Inordertobettercopewiththeseproblems,advancepredictionofphotovoltaicpowerisofgreatsignificancetosmartgridandenergyscheduling.

ResearchMethods:

2.1Introductionofchaoticsequences:Chaoticsequencesaredynamicsystemsequenceswithrandomness,non-periodicity,andsensitivedependenceoninitialvalues.Byintroducingchaoticsequences,thephotovoltaicpowerdatacanbepreprocessedtoreducetheinfluenceofnoiseandextractthepotentialregularitiesofthedata.

2.2RBFneuralnetwork:RBFneuralnetworkisacommonlyusedartificialneuralnetworkmodel.Byusingradialbasisfunctionastheactivationfunction,itcanhandlenon-linearproblemsandhasgoodapproximationandgeneralizationabilities.Inthispaper,RBFneuralnetworkisappliedtothephotovoltaicpowerpredictionmodel.

Constructionofchaotic-RBFneuralnetworkmodel:

3.1Datapreprocessing:First,collecttimeseriesdataofphotovoltaicpower.Then,usechaoticsequencestopreprocesstheoriginaldataandobtainpreprocesseddata.Byintroducingchaoticsequences,thecorrelationbetweendatacanbereducedandthepotentialregularitiesofthedatacanbeextractedtoprepareforsubsequentpredictionmodeling.

3.2Modeltrainingandvalidation:Dividethepreprocesseddataintotrainingsetandvalidationset.Trainthechaotic-RBFneuralnetworkmodelusingthetrainingset,adjustthemodelparameterscontinuouslytoimprovethefittingabilityofthemodel.Then,validatethemodelusingthevalidationsettoevaluatetheperformanceofthemodel.

Experimentsandresultanalysis:Verifytheproposedchaotic-RBFneuralnetworkmodelthroughexperiments.Selectactualphotovoltaicpowerdataassamplesandcompareitwithothercommonlyusedmodels.Theexperimentalresultsshowthattheproposedmodelhashigheraccuracyandreliabilityinphotovoltaicpowerultra-short-termprediction.

ConclusionandProspect:Thispaperproposesaphotovoltaicpowerultra-short-termpredictionmodelbasedonchaotic-RBFneuralnetwork.ByintroducingchaoticsequencesfordatapreprocessingandcombiningwithRBFneuralnetworkforprediction,theaccuracyandstabilityofthepredictionareimproved.Theexperimentsprovethatthemodelhasgoodperformanceinphotovoltaicpowerprediction.Futureresearchcanfurtheroptimizethestructureandalgorithmofthemodeltoimprovetheaccuracyandpracticalityofthe