Two-stage Transfer Learning Short-term Wind Power Prediction Based on Two-dimensional Wind Speed Correction and Multiple Integration

被引：0

作者：

Ma, Zhiyuan ^{[1
]}

Wang, Bo ^{[2
]}

Yang, Mao ^{[1
]}

Wang, Zhao ^{[2
]}

机构：

[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin,132012, China

[2] National Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing,100192, China

来源：

Gaodianya Jishu/High Voltage Engineering | 2024年 / 50卷 / 09期

关键词：

In order to improve the power prediction accuracy of newly-invested grid-connected wind farms with insufficient data; a method; which is named for two-stage transfer learning short-term wind power prediction; based on two-dimensional wind speed correction and multiple integration is proposed. Firstly; at data enhancement stage; the wind measurement from weather stations before connection to grid is used. The spatio-temporal correlation of wind farms is taken into consideration; and time series features are constructed and scenes are matched. The forecast wind speeds are preliminarily corrected in both temporal and spatial dimensions. Then; the preliminary corrected results are reconstructed as the input of the next integrated learning; and a multiple integrated learning model is constructed to correct forecast wind speed again. Finally; at power prediction stage; the forecast power is obtained by GRU based on the data correction. The results show that the proposed method can be adopted to reduce the root mean square error of the forecast wind speed by 1.038 m/s and improve the accuracy of power prediction by 4.718%. The research can provide a reference for newly-invested grid-connected wind farms power prediction. © 2024 Science Press. All rights reserved;

D O I：

10.13336/j.1003-6520.hve.20232115

中图分类号：

学科分类号：

摘要：

引用

页码：3934 / 3943

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