Research on Wind Power Forecasting Error Based on Gaussian Mixture Distribution Model

被引：0

作者：

Yan, Peng ^{[1
]}

Shi, Min ^{[2
]}

Wang, Tieqiang ^{[2
]}

Yin, Rui ^{[2
]}

Wang, Yifeng ^{[2
]}

机构：

[1] State Grid Hebei Elect Power Res Inst, Shijiazhuang, Hebei, Peoples R China

[2] State Grid Hebei Elect Power Co Ltd, Shijiazhuang, Hebei, Peoples R China

来源：

2021 3RD ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM (AEEES 2021) | 2021年

关键词：

wind power generation; forecasting error; Gaussian mixture distribution model; improved EM algorithm;

D O I：

10.1109/AEEES51875.2021.9403055

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In order to effectively reduce the influence of wind power uncertainty on the power grid and improve the safety of power system operation, it is necessary to carry out fine modeling of wind power forecasting error. Based on the actual wind farm operation data, this paper proposes a generalized Gaussian mixture model to describe the distribution characteristics of its forecasting errors, and uses an improved expectation maximization (EM) algorithm to solve the model parameters. The model can accurately describe the multi-peak and tailing in wind power forecasting errors and has good fitting effect. Finally, an example analysis is carried out based on the actual wind farm data, and compared with the commonly used normal distribution and t Location-Scale distribution models, which proves the effectiveness of the proposed model.

引用

页码：374 / 378

页数：5

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