A Short-term Wind Power Prediction Model Based on Graph Convolutional Neural Network-bidirectional Gated Recurrent Unit and Attention Mechanism

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New Energy Power System | 更新时间：2025-10-13

- A Short-term Wind Power Prediction Model Based on Graph Convolutional Neural Network-bidirectional Gated Recurrent Unit and Attention Mechanism
- “In the field of wind power prediction, experts have proposed a short-term wind power prediction model using graph convolutional neural networks with bidirectional gated recurrent units and attention mechanisms, providing a solution to improve prediction accuracy.”
- Modern Electric Power Vol. 42, Issue 2, Pages: 201-208(2025)
- 作者机构：
  
  新疆大学电气工程学院, 新疆维吾尔自治区乌鲁木齐市 830047
- 作者简介：
- 基金信息：
  
  Project Supported by National Natural Science Foundation of China (51667018); Natural Science Foundation of Xinjiang Uygur Autonomous Region (2021D01C044).
- DOI：10.19725/j.cnki.1007-2322.2023.0025
  CLC： TM614
- Received：29 January 2023，
  
  Accepted：01 December 2023，
  
  Published：10 April 2025
- 稿件说明：
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Guanghao ZHANG, Xinyan ZHANG, Pengkai WANG. A Short-term Wind Power Prediction Model Based on Graph Convolutional Neural Network-bidirectional Gated Recurrent Unit and Attention Mechanism[J]. Modern electric power, 2025, 42(2): 201-208.
DOI：

Guanghao ZHANG, Xinyan ZHANG, Pengkai WANG. A Short-term Wind Power Prediction Model Based on Graph Convolutional Neural Network-bidirectional Gated Recurrent Unit and Attention Mechanism[J]. Modern electric power, 2025, 42(2): 201-208. DOI： 10.19725/j.cnki.1007-2322.2023.0025.

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