融合时空特性的改进Informer风电功率短期预测

郭利进; 蔡昊翔; 孙淼

doi:10.19805/j.cnki.jcspe.2026.250018

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融合时空特性的改进Informer风电功率短期预测

新能源与储能 | 更新时间：2026-10-10

- 融合时空特性的改进Informer风电功率短期预测
- Short-term Wind Power Prediction Based on Improved Informer Incorporating Spatiotemporal Features
- 动力工程学报 2026年46卷第4期页码：101-109
- 作者机构：
  
  1. 天津工业大学控制科学与工程学院,天津,300387
  2. 天津市电气装备智能控制重点实验室,天津,300387
- 作者简介：
  
  郭利进(1970—),男,湖北黄冈人,教授,博士研究生,研究方向为过程参数的检测与控制,E-mail:Doctor_guo@tiangong.edu.cn。
- 基金信息：
  
  国家自然科学基金资助项目(52077155)
- DOI：10.19805/j.cnki.jcspe.2026.250018
  中图分类号： TM614
- 纸质出版：2026
- 稿件说明：
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郭利进, 蔡昊翔, 孙淼. 融合时空特性的改进Informer风电功率短期预测[J]. 动力工程学报, 2026, 46(4): 101-109.
郭利进, 蔡昊翔, 孙淼. 融合时空特性的改进Informer风电功率短期预测[J]. 动力工程学报, 2026, 46(4): 101-109. DOI： 10.19805/j.cnki.jcspe.2026.250018.

DOI： 10.19805/j.cnki.jcspe.2026.250018

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传统预测方法在捕捉风电场间时空相关性方面的局限性

提出一种新型风电功率预测模型。首先采用改进的嵌入方式

提出时间-特征混合多层感知机(MLP)

以强化特征维度间多重相关性的捕获能力

提取全局时序与动态特征的关联信息。然后

提出空间特征交互机制(SFI)

利用双通道输入能力构建基于注意力机制的特征提取模块

获取特定风电场与周边风电场之间的动态空间依赖关系。最后

将融合多电场的时空特征图输入到编码器

得到预测结果。通过多组实际数据对所提出的方法进行了实验分析与验证。结果表明:所提出的预测方法在预测精度方面优于其他预测方法。

Abstract

To address the low prediction accuracy caused by the instability of wind power series and the limitations of traditional prediction methods in capturing the spatiotemporal correlation between wind farms

a novel wind power prediction model was proposed. Firstly

by employing an improved embedding method

a temporal-feature hybrid multilayer perceptron (MLP) was proposed to enhance the ability to capture multiple correlations among feature dimensions and extract correlation information between global temporal and dynamic features. Then

the spatial feature interaction (SFI) mechanism was proposed. Utilizing dual-channel input capability

a feature extraction module based on attention mechanism was constructed to acquire dynamic spatial dependencies between the target wind farm and surrounding wind farms. Finally

the spatiotemporal feature map incorporating multiple wind farms was input into the encoder to obtain the prediction results. The proposed method was experimentally analyzed and verified by multiple sets of actual data. Results show that the proposed prediction method outperforms other prediction methods in terms of prediction accuracy.

关键词

Keywords

references

陈子含, 滕伟, 胥学峰, 等. 基于图卷积网络和风速差分拟合的中长期风功率预测[J]. 中国电力, 2023, 56(10): 96-105.CHEN Zihan, TENG Wei, XU Xuefeng, et al. Medium and long term wind power prediction based on graph convolutional network and wind velocity differential fitting[J]. Electric Power, 2023, 56(10): 96-105.

魏乐, 戴泽, 陈远野, 等. 考虑多对一时空特征的短期风功率组合预测模型[J]. 动力工程学报, 2024, 44(12): 1869-1877.WEI Le, DAI Ze, CHEN Yuanye, et al. Combined prediction model of short-term wind power by considering many-to-one spatial-temporal features[J]. Journal of Chinese Society of Power Engineering. 2024, 44(12): 1869-1877.

WANG Yi, ZHANG Ning, KANG Chongqing, et al. An efficient approach to power system uncertainty analysis with high-dimensional dependencies[J]. IEEE Transactions on Power Systems, 2018, 33(3): 2984-2994.

ERDEM E, SHI Jing. ARMA based approaches for forecasting the tuple of wind speed and direction[J]. Applied Energy, 2011, 88(4): 1405-1414.

高晟扬, 李法社. 基于CEEMD-SE-PSR-BP的短期风速预测[J]. 太阳能学报, 2025, 46(4): 415-422.GAO Shengyang, LI Fashe. Short-term wind speed forecasting based on CEEMD-SE-PSR-BP method[J]. Acta Energiae Solaris Sinica, 2025, 46(4): 415-422.

LI Daoqing, YU Xiaodong, LIU Shulin, et al. Wind power prediction based on PSO-Kalman[J]. Energy Reports, 2022, 8(Sup4): 958-968.

SAMADIANFARD S, HASHEMI S, KARGAR K, et al. Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm[J]. Energy Reports, 2020, 6: 1147-1159.

LIU Erjia, WANG Yuxuan, HUANG Yongzhang. Short-term forecast of multi-load of electrical heating and cooling in regional integrated energy system based on deep LSTM RNN[C]//Proceedings of 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). Wuhan: IEEE, 2020: 2994-2998.

付炳喆, 王玮, 任国瑞, 等. 考虑增强特征选择的深度卷积-时序网络短期风功率预测[J]. 动力工程学报, 2024, 44(10): 1565-1573.FU Bingzhe, WANG Wei, REN Guorui, et al. Short-term wind power prediction with deep convolutional-temporal networks considering enhanced feature selection[J]. Journal of Chinese Society of Power Engineering, 2024, 44(10): 1565-1573.

LV Yunlong, HU Qin, XU Hang, et al. An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model[J]. Energy, 2024, 293: 130751.

PENG Xiaosheng, CHENG Kai, LANG Jianxun, et al. Short-term wind power prediction for wind farm clusters based on SFFS feature selection and BLSTM deep learning[J]. Energies, 2021, 14(7): 1894.

周家慷. 基于深度学习的风电集群短期功率预测方法研究[D]. 北京: 华北电力大学, 2021.

BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[C]//Proceedings of the 3rd International Conference on Learning Representations. San Diego: ICLR, 2015: 1-15.

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 6000-6010.

SHEN Li, WEI Yuning, WANG Yangzhu, et al. Take an irregular route: enhance the decoder of time-series forecasting transformer[J]. IEEE Internet of Things Journal, 2024, 11(8): 14344-14356.

ZHOU Haoyi, ZHANG Shanghang, PENG Jieqi, et al. Informer: beyond efficient transformer for long sequence time-series forecasting[C]//Proceedings of the AAAI conference on Artificial Intelligence. Canada:[s.n.],2021.

ZHANG Yunhao, YAN Junchi. Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting[C]//Proceedings of the 11th International Conference on Learning Representations. Kigali: ICLR, 2023: 1-21.

ZHOU Tian, MA Ziqing, WEN Qingsong, et al. FEDFormer: frequency enhanced decomposed transformer for long-term series forecasting[C]//Proceedings of the 39th International Conference on Machine Learning. Baltimore: PMLR, 2022: 27268-27286.

乔石, 王磊, 张鹏超, 等. 基于时间模式注意力机制的GRU短期负荷预测[J]. 电力系统及其自动化学报, 2023, 35(10): 49-58.QIAO Shi, WANG Lei, ZHANG Pengchao, et al. Short-term load prediction of GRU neural network based on temporal pattern attention mechanism[J]. Proceedings of the CSU EPSA, 2023, 35(10): 49-58.

李昊. 基于时间和空间特征挖掘的风电功率超短期预测[D]. 徐州: 中国矿业大学, 2022.

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