Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (5): 70-76.doi: 10.6040/j.issn.1672-3961.0.2019.706

Previous Articles    

Interval prediction of short-term regional photovoltaic power based on BP neural network

SUN Donglei1, WANG Yan1, YU Yixiao2*, HAN Xueshan2, YANG Ming2, YAN Fangqing2   

  1. 1. Economic and Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China;
    2. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2020-10-19

Abstract: High penetration photovoltaic power brought severe challenges to the sage and stable operation of power systems. Considering the limitations of individual photovoltaic power prediction, this paper proposed an interval prediction method of regional photovoltaic power based on BP neural network, which extracted correlation through mutual information, and applied principal component analysis(PCA)to data dimensionality reduction. Taking the advantages of the data mining and nonlinear relation fitting for BP neural network, the uncertainty of regional photovoltaic power prediction could be quantified by the combining the neural networks and nonparametric probabilistic prediction methods. Experimental example used 10 photovoltaic plants in an area in China and compared the method of no data dimension reduction of original data with the proposed method in this paper. The 80% and 90% confidence intervals of the two models were calculated, which showed that the confidence intervals of the proposed mothed were narrower. At the same time, the proposed model predicted the 80% and 90% confidence intervals with 72 hours in advance, the results verified the feasibility and advance of the proposed method.

Key words: regional photovoltaic power, prediction intervals, BP neural network, dimensionality reduction, principal component analysis

CLC Number: 

  • TM60
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