改进Informer网络的风电功率短期预测
作者:
基金项目:

国家重点研发计划 (2018YFB1403303); 辽宁省教育厅高校科研基金 (2021LJKZ0327)


Short-term Wind Power Prediction Based on Improved Informer Network
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    准确预测风电功率对于提高电力系统的效率和安全性具有重要意义, 而风能的间歇性和随机性特点导致风电功率难以准确预测. 因此, 提出一种改进Informer的风电功率预测模型PCI-Informer (PATCH-CNN-IRFFN-Informer). 将序列数据划分为子序列级补丁, 并进行特征提取和整合, 提高模型对序列数据的处理能力和效果; 采用多尺度因果卷积自注意力机制, 实现多尺度局部特征融合, 提高模型对局部信息的理解和建模能力; 引入反向残差前馈网络 (IRFFN), 增强模型对局部结构信息的提取和保留能力. 某风电场数据实验结果表明, 与主流预测模型相比, PCI-Informer模型在不同预测步长下均取得了更好的预测效果, 在MAE指标上相比Informer模型平均降低了11.1%, 有效提高了短期风电功率的预测精度.

    Abstract:

    Accurately predicting wind power is of great significance for improving the efficiency and safety of the power system, while the intermittence and randomness of wind energy make it difficult to predict wind power accurately. Therefore, an improved wind power prediction model based on Informer, namely PCI-Informer (PATCH-CNN-IRFFN-Informer) is proposed. The sequence data is divided into subsequence-level patches for feature extraction and integration, which improves the model’s ability to process sequence data and its effectiveness. Multiple-scale causal convolution self-attention mechanism is used to achieve multi-scale local feature fusion, which enhances the model’s understanding and modeling ability of local information. The inverse residual feedforward network (IRFFN) is introduced to enhance the model’s ability to extract and preserve local structural information. Experiment verification is conducted using data from a wind farm, and the results show that compared with mainstream prediction models, the PCI-Informer model achieves better prediction performance at different prediction time steps, with an average reduction of 11.1% in MAE compared with the Informer model, effectively improving the short-term wind power prediction accuracy.

    参考文献
    相似文献
    引证文献
引用本文

陈万志,戎馨鑫,王天元.改进Informer网络的风电功率短期预测.计算机系统应用,2024,33(5):118-126

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2023-11-02
  • 最后修改日期:2023-12-22
  • 在线发布日期: 2024-04-01
文章二维码
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号