| 丁 明,虞海彪,刘 练,毕 锐,张 超.基于多变量相空间重构和 RBF 神经网络的光伏功率预测方法[J].电子测量与仪器学报,2020,34(8):1-7 |
| 基于多变量相空间重构和 RBF 神经网络的光伏功率预测方法 |
| Power prediction method of photovoltaic generation based on multivariablephase space reconstruction and RBF neural network |
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| DOI： |
| 中文关键词: 光伏功率 气象因素 多变量相空间重构 Pearson 相关系数 RBF 神经网络 |
| 英文关键词:PV power meteorological factors multivariate phase space reconstruction Pearson correlation coefficient RBF neural network |
| 基金项目:国家重点研发计划(2016YFB0900400)、可再生能源与工业节能安徽省工程实验室开放课题(45000-411104 / 012)资助项目 |
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| 中文摘要: |
| 针对光伏功率单变量预测方法的不足,设计了一种新型光伏功率多变量相空间重构预测方法。 首先,基于相关性分析,
选取实际光伏电站的历史光伏功率和气象因素时间序列组成多变量时间序列;然后,利用 C-C 法和虚假邻近点( false nearest
neighbors,FNN)法重构光伏功率预测的多变量相空间,并以小数据法识别其混沌特性;最后,结合径向基函数( radial basis
function,RBF)神经网络强大的非线性拟合能力,建立了基于多变量相空间重构和 RBF 神经网络的光伏功率预测模型。 算例分
析表明,相较于单变量预测方法,所提出的多变量相空间重构预测方法性能更加优越。 |
| 英文摘要: |
| In view of the shortcomings of the single variable prediction method of photovoltaic (PV) power, a new multivariable phase
space reconstruction prediction method of PV power is designed. Firstly, based on the correlation analysis, the historical PV power and
meteorological factors time series of the actual PV power plant are selected to form multivariate time series. Then, the multivariable
phase space of PV power prediction is reconstructed by C-C method and false nearest neighbors ( FNN) method, and its chaotic
characteristics are identified by small data method. Finally, combined with the powerful nonlinear fitting ability of radial basis function
(RBF) neural network, a PV power prediction model based on multivariate phase space reconstruction and RBF neural network is
established. The example analysis shows that the proposed multivariate phase space reconstruction prediction method has better
performance than the single variable prediction method. |
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