SHORT-TERM PREDICTION METHOD OF WIND POWER CLUSTERS BASED ON GRAPH CONVOLUTION NEURAL NETWORK UNDER SPITIO-TEMPORAL CHARACTERISTICS

被引：0
|
作者：
Qiao K. [1 ,2 ]
Dong C. [1 ,3 ]
Che J. [2 ]
Jiang J. [1 ]
Wang B. [2 ]
机构：
[1] School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou
[2] National Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing
[3] National Electric Power Dispatching and Control Center, Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 05期
关键词：
attention mechanism; deep learning; graph convolutional neural network; graph data structures; spatio-temporal characteristics; wind power;
D O I：
10.19912/j.0254-0096.tynxb.2023-0009
中图分类号：
学科分类号：
摘要：
In order to solve the problems that the traditional wind power clusters prediction methods ignore the meteorological correlation characteristics of different locations and the single site prediction cannot quickly obtain the overall power of the wind power clusters，and fully consider the complex spatio-temporal characteristics of wind power clusters coupling，a short-term prediction method of wind power clusters based on attention mechanism and spatio-temporal graph convolution neural network is proposed. Initially，the mutual information between the historical power of wind farms in the region is calculated，the feature adjacency matrix is extracted，and the meteorological characteristic variables that affect the cluster power，which are converted into meteorological graph data. Furthermore，a graph convolution network（GCN）model is constructed to extract the correlation characteristics of meteorological graph nodes from non-European space. The gated recurrent unit（GRU）network，which incorporates the attention mechanism（AM），is fed to enhance the contribution of key information in the temporal features to the power of wind power clusters. Finally，the progressiveness and adaptability of the proposed method is verified based on the actual operation data of the wind power cluster in a certain province in Western China. © 2024 Science Press. All rights reserved.
引用
收藏
页码：95 / 103
页数：8
相关论文
共 21 条
  • [1] WANG B, FENG S L，, Et al., Prediction method for short- term wind power based on wind farm clusters［J］, High voltage engineering, 44, 4, pp. 1254-1260, (2018)
  • [2] TU Q Y, MIAO S H, LIN Y J，, Et al., Ultra- short- term interval forecasting method for regional wind farms based on dynamic R-vine Copula model ［J］, High voltage engineering, 48, 2, pp. 456-470, (2022)
  • [3] WANG Y J, Et al., Short-term regional wind power statistical upscaling forecasting based on feature clustering［J］, Power system technology, 41, 5, pp. 1383-1389, (2017)
  • [4] LI C, PENG X S, WANG H H，, Et al., Short-term power prediction of wind power cluster based on SDAE deep learning and multiple integration［J］, High voltage engineering, 48, 2, pp. 504-512, (2022)
  • [5] YANG G Q, LIU S L, WANG D Y，, Et al., Short-term wind power forecasting based on Attention- GRU wind speed correction and Stacking［J］, Acta energiae solaris sinica, 43, 12, pp. 273-281, (2022)
  • [6] PENG X S，, CHENG K，, LANG J X，, Et al., Short-term wind power prediction for wind farm clusters based on SFFS feature selection and BLSTM deep learning［J］, Energies, 14, 7, (2021)
  • [7] ZHOU J K., Research on short- term power forecasting method of wind power cluster based on deep learning［D］, (2021)
  • [8] CHEN G, SHAN J N, LI D Y，, Et al., Research on wind power prediction method based on convolutional neural network and genetic algorithm, 2019 IEEE Innovative Smart Grid Technologies- Asia （ISGT Asia）, pp. 3573-3578, (2019)
  • [9] YIN H, CHEN D, Et al., Ultra- short- term wind power prediction based on two- layer mode decomposition and cascaded deep learning［J］, Power system technology, 44, 2, pp. 445-453, (2020)
  • [10] ZHANG S Q, YANG Z N，, JIANG A Q，, Et al., Short term wind power prediction based on EN-SKPCA dimensionality reduction and FPA optimizing LSTMNN［J］, Acta energiae solaris sinica, 43, 6, pp. 204-211, (2022)
123