融合改进Informer与迁移学习的风电功率预测

郭利进; 孙淼; 衡安阳

doi:10.19912/j.0254-0096.tynxb.2024-0425

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (7) : 371-377. DOI: 10.19912/j.0254-0096.tynxb.2024-0425

第二十七届中国科协年会学术论文

融合改进Informer与迁移学习的风电功率预测

郭利进^1,2, 孙淼^1,2, 衡安阳^1,2

作者信息 +

WIND POWER PREDICTION BASED ON IMPROVED INFORMER AND TRANSFER LEARNING

Guo Lijin^1,2, Sun Miao^1,2, Heng Anyang^1,2

Author information +

文章历史 +

摘要

为克服风电功率序列的不稳定性导致预测精度低以及一些风电场历史数据有限的问题,提出一种特征交互Informer与迁移学习（FIITL）的风电功率预测模型。首先提出特征交互（FI）机制用双通道输入进一步提取信息,并将迁移学习（TL）引入到预测模型中,提出循环微调迁移学习,将模型从源监测站迁移到目标站,实现在有限历史数据情况下预测性能的提升。最后,通过与传统Informer模型及其他基线预测方法比较,FIITL模型展现了在有限数据情况下的性能优势。

Abstract

To overcome the instability of wind power sequences resulting in low prediction accuracy and the limited historical data of some wind farms, we propose a wind power prediction model called feature interaction in Informer with transfer learning （FIITL）. Firstly, we introduce a feature interaction （FI） amechanism with dual-channel input to further extract information. Secondly, transfer learning （TL） is incorporated into the prediction model, resulting in a cyclic fine-tuning transfer learning method. This method transfers the model from a source monitoring station to a target station, thereby improving predictive performance under limited historical data. Finally, the FIITL model is compared with traditional Informer models and other baseline prediction methods. The results demonstrate that the FIITL model outperforms these models in situations with limited data.

导出引用

郭利进, 孙淼, 衡安阳. 融合改进Informer与迁移学习的风电功率预测[J]. 太阳能学报. 2025, 46(7): 371-377 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0425

Guo Lijin, Sun Miao, Heng Anyang. WIND POWER PREDICTION BASED ON IMPROVED INFORMER AND TRANSFER LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 371-377 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0425

中图分类号： TM614

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