基于迁移学习CNN-LSTM的海上风电场风速及功率高精度短期预测方法

余彪; 王卓恒; 綦晓; 孙单勋; 臧兴海; 盛发明

doi:doi:10.19912/j.0254-0096.tynxb.2024-2060

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基于迁移学习CNN-LSTM的海上风电场风速及功率高精度短期预测方法

更新时间：2026-07-22

- 基于迁移学习CNN-LSTM的海上风电场风速及功率高精度短期预测方法
- Vol. 47, Issue 4, Pages: 222-230(2026)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2060
  CLC： TM614
- Online First：08 May 2026，
  
  Published：2026
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余彪, 王卓恒, 綦晓, et al. 基于迁移学习CNN-LSTM的海上风电场风速及功率高精度短期预测方法[J]. 2026, 47(4): 222-230.
DOI：

余彪, 王卓恒, 綦晓, et al. 基于迁移学习CNN-LSTM的海上风电场风速及功率高精度短期预测方法[J]. 2026, 47(4): 222-230. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2060.

摘要

在海上风电场景中

风速及功率的短期预测面临诸多挑战

常规长短期记忆神经网络（LSTM）难以准确预测深远海域风电场表现。为此

该研究在广东某海上风电场部署了多普勒激光雷达

并收集了实测风速数据

针对预测精度不足的原因

提出基于迁移学习的卷积-长短期记忆神经网络CNN-LSTM预测模型。迁移学习能通过利用相似任务的知识加速新任务学习并提升模型性能;CNN擅长提取雷达数据的空间特征;LSTM则擅长捕捉时间序列的长期依赖性。综合上述特点

使该文提出的方法在提升海上风电风速预测和功率预测方面表现出色。实验结果显示

与雷达测量数据及常规LSTM模型相比

新方法在预测海上风速和风电功率时误差显著降低。

Abstract

In offshore wind power scenarios

short-term prediction of wind speed and power faces many challenges. Conventional long short-term memory neural networks （LSTM） struggle to accurately predict wind speed and power in far-reaching offshore wind farms. To address this challenge

this study deploys a Doppler lidar in an offshore wind farm in Guangdong

and collects measured wind speed data. To address the reasons for the insufficient prediction accuracy

this paper proposes a convolutional neural network-long short-term memory （CNN-LSTM） prediction model based on migration learning. Migration learning can accelerate new task learning and improve model performance by utilizing knowledge from similar tasks; CNN is good at extracting spatial features of radar data; and LSTM is good at capturing long-term dependencies of time series. The combination of these features makes the method proposed in this paper perform well in improving wind speed prediction and power prediction for offshore wind power. The experimental results show that compared with the original radar data and the conventional LSTM model

the new method significantly reduces the error in predicting the offshore wind speed and wind power

and the prediction accuracy is better improved.

关键词

Keywords

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