陈文进,陈菁伟,钱建国,唐明,林承钱,许一洲,刘皓明
CHEN Wenjin,CHEN Jingwei,QIAN Jianguo,TANG Ming,LIN Chengqian,XU Yizhou,LIU Haoming

• 1:国网浙江省电力有限公司
• 2:国网浙江省电力有限公司湖州供电公司
• 3:河海大学能源与电气学院

摘要(Abstract)：

准确的光伏功率预测对电网稳定运行具有重要意义，因此提出一种气象特征频繁变化区域的光伏功率预测方法，以期能提高光伏功率预测的准确性。首先，基于Person相关性分析构建光伏功率预测的多变量时间序列；然后，利用C-C法对光伏功率预测的时间序列进行MPSR（多变量相空间重构），进一步挖掘光伏功率与气象特征间的耦合关系；最后，利用SVR（支持向量回归）对光伏功率与气象特征重构后的相空间进行非线性拟合并预测。为验证MPSR能够提高预测效果，同时比较了MPSR结合BPNN（反向传播神经网络）与RBFNN（径向基函数神经网络）的预测效果。算例分析表明，MPSR能够进一步挖掘气象特征变化频繁区域中包含的特征信息，结合MPSR的预测模型提高了光伏功率的预测精度。
Accurate prediction of photovoltaic(PV) power is of great significance to the stable operation of power grid. Therefore, a PV power prediction method is proposed based on frequent changes of meteorological features to improve the prediction accuracy. Firstly, the multivariate time series of PV power prediction is constructed based on Person correlation analysis. Secondly, multivariable phase space reconstruction(MPSR) is performed for the time series of PV power prediction by C-C method to further investigate the coupling between PV power and meteorological characteristics. Finally, support vector regression(SVR) is used for non-linear fitting and predicting the phase space after PV power and meteorological feature reconstruction. To verify MPSR can improve the prediction effect, the paper compares MPSR that combines with back propagation neural network(BPNN) and radial basis function neural network(RBFNN). Example analysis shows that MPSR can further explore the feature information contained in the regions with frequent changes of meteorological features. The prediction model that combines with MPSR improves the prediction accuracy of PV power.

关键词(KeyWords)： 光伏功率预测;特征挖掘;多变量相空间重构;支持向量回归
photovoltaic power prediction;characteristics mining;multivariable phase space reconstruction;support vector regression

Abstract:

Keywords:

基金项目(Foundation): 浙江省电力有限公司科技项目（5211UZ2000K4）

作者(Author): 陈文进,陈菁伟,钱建国,唐明,林承钱,许一洲,刘皓明
CHEN Wenjin,CHEN Jingwei,QIAN Jianguo,TANG Ming,LIN Chengqian,XU Yizhou,LIU Haoming

DOI: 10.19585/j.zjdl.202303005

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