Neural Network Ensemble Method Study for Wind Power Prediction

被引:0
|
作者
Han, Shuang [1 ]
Liu, Yongqian [1 ]
Yan, Jie [1 ]
机构
[1] North China Elect Power Univ, Sch Renewable Energy, Beijing, Peoples R China
关键词
wind power prediction; neural network ensemble; individual neural network; difference scale; prediction precision; SPEED;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Wind power prediction is of great importance for the safety, stabilization and economic efficiency of electric power grids, especially when the wind power penetration level of the gird is high. ANN (Artificial Neural Network) is an appropriate method for wind power prediction. But the generalization of common ANN is poor and the prediction precision is not stable. Neural network ensemble can enhance the generalization ability of neural network remarkably. Neural network ensemble has two key problems: one is how to build individual neural network, and the other is how to synthesize the outputs of the individual networks. According to wind power prediction characteristic, a new method was used to build individual neural network, the different individual neural network can be given specific physical meaning. ANN was used to synthesize the outputs of the individual networks. The calculation example showed that the difference scale between each individual neural network was higher and the prediction precision was greatly improved compared to that of the single neural network.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] Wind power prediction by cascaded clustering method and wavelet neural network
    Sun, Gaiping
    Jiang, Chuanwen
    [J]. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2021, 42 (03): : 56 - 62
  • [2] Ensemble Neural Network Method for Wind Speed Forecasting
    Yong, Binbin
    Qiao, Fei
    Wang, Chen
    Shen, Jun
    Wei, Yongqiang
    Zhou, Qingguo
    [J]. PROCEEDINGS OF THE 2019 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS 2019), 2019, : 31 - 36
  • [3] A Wind Power Prediction Method Based on DE-BP Neural Network
    Li, Ning
    Wang, Yelin
    Ma, Wentao
    Xiao, Zihan
    An, Zhuoer
    [J]. FRONTIERS IN ENERGY RESEARCH, 2022, 10
  • [4] Wind Power Prediction Based on a Convolutional Neural Network
    Zhu, Anwen
    Li, Xiaohui
    Mo, Zhiyong
    Wu, Huaren
    [J]. CONFERENCE PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON CIRCUITS, DEVICES AND SYSTEMS (ICCDS), 2017, : 131 - 135
  • [5] Wind Power Prediction Based on Genetic Neural Network
    Zhang, Suhan
    [J]. 2017 5TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2017), 2017, 1834
  • [6] Wind farm wind power prediction method based on CEEMDAN and DE optimized DNN neural network
    Zhang, Qun
    Tang, Zhenhao
    Cao, Shengxian
    Wang, Gong
    [J]. 2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 1626 - 1630
  • [7] Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction
    Santhosh, Madasthu
    Venkaiah, Chintham
    Kumar, D. M. Vinod
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2018, 168 : 482 - 493
  • [8] A new wind power prediction method based on chaotic theory and Bernstein Neural Network
    Wang, Cong
    Zhang, Hongli
    Fan, Wenhui
    Fan, Xiaochao
    [J]. ENERGY, 2016, 117 : 259 - 271
  • [9] Research on wind power Prediction based on BP neural Network
    Hu, Dongmei
    Zhang, Zhaoyun
    Zhou, Hao
    [J]. 2022 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, COMPUTING, COMMUNICATION AND SUSTAINABLE TECHNOLOGIES (ICAECT), 2022,
  • [10] Neural network and improved method for wind power prediefion
    Li, Rui
    [J]. PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (ICAISE 2013), 2013, 37 : 199 - 203