Short-term Photovoltaic Power Prediction Method Combining With Radiation Attenuation Factor Prediction and RBF Neural Network

被引：0
|
作者：
Liang Z. [1 ]
Dong C. [1 ]
Wu J. [2 ]
Cui F. [2 ]
Chen W. [2 ]
机构：
[1] State Grid Corporation of China, Xicheng District, Beijing
[2] China Electric Power Research Institute, Nanjing
来源：
Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 11期
关键词：
Photovoltaic short-term power prediction; PM2.5; RBF neural network; Total cloud cover;
D O I：
10.13335/j.1000-3673.pst.2019.2353
中图分类号：
学科分类号：
摘要：
In order to improve the prediction accuracy of photovoltaic power under the complex cloudy and haze weather conditions, a short-term photovoltaic power prediction method is proposed combining with radiation attenuation factor prediction and RBF neural network. Using the WRF mesoscale model (V4.1) and the WRF-CHEM air quality model to realize the simulation of the total cloud amount and PM2.5 concentration, a direct mapping relationship model with the multi-meteorological elements and the photovoltaic output is constructed based on RBF neural network combined with the historical output data of the photovoltaic power station. The results show that the proposed method can effectively improve the accuracy of photovoltaic power prediction under cloudy and haze weather conditions, thus providing a strong support for the grid dispatching operation. © 2020, Power System Technology Press. All right reserved.
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页码：4114 / 4120
页数：6
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