基于混沌-RBF神經(jīng)網(wǎng)絡的光伏發(fā)電功率超短期預測模型

基于混沌-RBF神經(jīng)網(wǎng)絡的光伏發(fā)電功率超短期預測模型基于混沌-RBF神經(jīng)網(wǎng)絡的光伏發(fā)電功率超短期預測模型

摘要：光伏發(fā)電作為一種清潔、可持續(xù)的能源，受到越來越多的關注。為了更好地利用光伏發(fā)電，準確預測光伏發(fā)電功率對智能電網(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)絡的光伏發(fā)電功率超短期預測模型，通過引入混沌序列作為數(shù)據(jù)預處理方法，并結合RBF神經(jīng)網(wǎng)絡進行預測，提高了預測的準確性和穩(wěn)定性。實驗結果表明，該模型能夠有效地預測光伏發(fā)電功率，在超短期預測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)境保護意識的提高，光伏發(fā)電被廣泛應用。然而，光伏發(fā)電受天氣、環(huán)境等因素的影響，具有波動性強、不穩(wěn)定的特點。為了更好地應對這些問題，提前進行光伏發(fā)電功率的預測，對智能電網(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)進行預處理，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

RBF神經(jīng)網(wǎng)絡是一種常用的人工神經(jīng)網(wǎng)絡模型。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)絡應用于光伏發(fā)電功率預測模型中。

3.混沌-RBF神經(jīng)網(wǎng)絡模型的構建

3.1數(shù)據(jù)預處理

首先，收集光伏發(fā)電功率的時間序列數(shù)據(jù)。然后，利用混沌序列對原始數(shù)據(jù)進行處理，得到預處理后的數(shù)據(jù)。通過混沌序列的引入，可以減少數(shù)據(jù)間的相關性，提取出數(shù)據(jù)的潛在規(guī)律，為后續(xù)的預測建模做準備。

3.2模型訓練與驗證

將預處理后的數(shù)據(jù)劃分為訓練集和驗證集。利用訓練集對混沌-RBF神經(jīng)網(wǎng)絡模型進行訓練，不斷調(diào)整模型參數(shù)，提高模型的擬合能力。然后，利用驗證集對模型進行驗證，評估模型的性能。

4.實驗與結果分析

通過實驗對所提出的混沌-RBF神經(jīng)網(wǎng)絡模型進行驗證。選取實際光伏發(fā)電功率數(shù)據(jù)作為樣本，并與其他常用模型進行比較。實驗結果表明，所提出的模型在光伏發(fā)電功率超短期預測中具有較高的精度和可靠性。

5.總結與展望

本文提出了一種基于混沌-RBF神經(jīng)網(wǎng)絡的光伏發(fā)電功率超短期預測模型。通過引入混沌序列進行數(shù)據(jù)預處理，并結合RBF神經(jīng)網(wǎng)絡進行預測，提高了預測的準確性和穩(wěn)定性。實驗證明，該模型在光伏發(fā)電功率預測中具有較好的效果。未來的研究可以進一步優(yōu)化模型的結構和算法，提高預測的精度和實用性。

關鍵詞：光伏發(fā)電；功率預測；混沌序列；RBF神經(jīng)網(wǎng)絡；超短期預測。

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