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- Title
气象特征频繁变化区域的光伏功率预测方法.
- Authors
陈文进; 陈菁伟; 钱建国; 唐 明; 林承钱; 许一洲; 刘皓明
- Abstract
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.
- Subjects
RADIAL basis functions; BACK propagation; POWER series; STATISTICAL correlation; ELECTRIC power distribution grids; STATISTICAL power analysis
- Publication
Zhejiang Electric Power, 2023, Vol 42, Issue 3, p37
- ISSN
1007-1881
- Publication type
Article
- DOI
10.19585/j.zjdl.202303005