Parallel Prediction of Offshore Wind Power Based on DAM and CNN-LSTM-XGBoost

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更新时间：2023-07-24

- Parallel Prediction of Offshore Wind Power Based on DAM and CNN-LSTM-XGBoost
- “在海上风电功率预测领域，研究者提出了一种融合双阶段注意力机制与CNN-LSTM-XGBoost的模型，通过贝叶斯优化和模型权重分配，显著提升了预测准确率，为并行预测提供了有效参考。”
  
  ”
- Software Guide Vol. 22, Issue 7, Pages: 27-31(2023)
- 作者机构：
  
  1.盐城工学院机械工程学院/优集学院，江苏盐城 224051
  2.华能海上风电科学技术研究有限公司，江苏盐城 224007
  3.华能国际电力江苏能源开发有限公司清洁能源分公司，江苏南京 210000
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.221779
  CLC： TM614
- Published：30 July 2023，
  
  Received：12 July 2022，
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程艳,张波,姚中原等.基于DAM与CNN-LSTM-XGBoost的海上风电功率并行预测[J].软件导刊,2023,22(07):27-31.

CHENG Yan,ZHANG Bo,YAO Zhongyuan,et al.Parallel Prediction of Offshore Wind Power Based on DAM and CNN-LSTM-XGBoost[J].Software Guide,2023,22(07):27-31.
程艳,张波,姚中原等.基于DAM与CNN-LSTM-XGBoost的海上风电功率并行预测[J].软件导刊,2023,22(07):27-31. DOI： 10.11907/rjdk.221779.

CHENG Yan,ZHANG Bo,YAO Zhongyuan,et al.Parallel Prediction of Offshore Wind Power Based on DAM and CNN-LSTM-XGBoost[J].Software Guide,2023,22(07):27-31. DOI： 10.11907/rjdk.221779.

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