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电网技术  2015 

基于机器学习的自适应光伏超短期出力预测模型

DOI: 10.13335/j.1000-3673.pst.2015.02.002, PP. 307-311

Keywords: 自适应预测,自回归和滑动平均模型,神经网络,小波分析,超短期光伏出力预测

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Abstract:

由于当前国内对太阳辐射强度和云量信息的预报能力较低,气象数据的引入对光伏直接预测法的预测精度提高有限。为解决此问题,基于历史出力数据自身特征的挖掘来提高预测精度,提出一种具有自适应能力的光伏超短期出力预测模型。该模型首先利用已有历史出力数据的小波分析和特征分析结果训练支持向量机(supportvectormachine,SVM)分类器,通过已建立的SVM分类器利用前30min的光伏出力数据预测之后15min的出力曲线类型,最后结合曲线类型从自回归与滑动平均模型(auto-regressiveandmovingaveragemodel,ARMA)和神经网络模型(artificialneuralnetworkmode,ANN)中选取出合适的方法对光伏出力进行预测。对ARMA、ANN和自适应模型进行了对比实验,结果表明所提的自适应预测模型在均方根误差(rootmeansquareerror,RMSE)、平均绝对百分比误差(meanabsolutepercentageerror,MAPE)和希尔不等系数(Theilinequalitycoefficient,TIC)上性能最好。

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