基于多方面特征提取和迁移学习的风速预测

梁涛; 陈春宇; 谭建鑫; 井延伟

doi:10.19912/j.0254-0096.tynxb.2021-1535

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (4) : 132-139. DOI: 10.19912/j.0254-0096.tynxb.2021-1535

基于多方面特征提取和迁移学习的风速预测

梁涛¹, 陈春宇¹, 谭建鑫², 井延伟²

作者信息 +

WIND SPEED PREDICTION BASED ON MULTIPLE FEATURE EXTRACTION AND TRANSFER LEARNING

Liang Tao¹, Chen Chunyu¹, Tan Jianxin², Jing Yanwei²

Author information +

文章历史 +

摘要

为满足风电场远程集控中心高效、低成本预测不同地理位置风电场风速的要求,结合“离线训练,在线预测”的思想,提出一种基于多方面特征提取和迁移学习的多变量风速预测模型。离线模型融合双通道卷积神经网络和双向长短时记忆神经网络捕捉风速信息,学习各典型位置风电场的风速特性,然后迁移至任意风电场实现快速在线预测,通过改进的多目标蝗虫优化算法集成各典型风电场预测结果,进一步提高预测精度。最后通过河北一集控中心验证表明,该文所提模型的适应性与准确性均优于其他基线模型。

Abstract

In order to meet the requirements of remote control centers for efficient and low-cost wind speed prediction of wind farms at different locations, this paper proposes a multivariable wind speed prediction model based on multiple feature extraction and transfer learning by combining the idea of "offline training, online prediction". The offline model fuses wind speed information captured by two-channel convolutional neural network and bi-directional long-short-term memory neural network. Wind speed characteristics of wind farms at typical locations are learned, and then the wind speed characteristics are transferred to other wind farms to achieve online prediction. The prediction accuracy is further improved by using an improved multi-objective grasshopper optimization algorithm, which integrates the prediction results of each typical wind farm. Finally, the superiority of the model is verified by the data of a centralized control center in Hebei. The results show that the adaptability and accuracy of the proposed model are superior than that of other baseline models.

导出引用

梁涛, 陈春宇, 谭建鑫, 井延伟. 基于多方面特征提取和迁移学习的风速预测[J]. 太阳能学报. 2023, 44(4): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1535

Liang Tao, Chen Chunyu, Tan Jianxin, Jing Yanwei. WIND SPEED PREDICTION BASED ON MULTIPLE FEATURE EXTRACTION AND TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1535

中图分类号： TK89 TM614

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