Short-Term Wind Power Prediction by Using the Deep Kernel Extreme Learning Machine with Well-Selected and Optimized Features

被引：2
|
作者：
Shang L. [1 ]
Huang C. [1 ]
Hou Y. [1 ]
Li H. [1 ]
Hui Z. [1 ]
Zhang J. [1 ]
机构：
[1] School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2023年 / 57卷 / 01期
关键词：
deep hybrid kernel extreme learning machine; feature optimization; improved dingo optimization algorithm; kernel principal component analysis; short-term wind power prediction;
D O I：
10.7652/xjtuxb202301007
中图分类号：
学科分类号：
摘要：
Aiming at the problem that wind power output is nonlinear, unstable and difficult to be accurately predicted by traditional methods, this paper proposes a short-term wind power prediction based on the optimization of parameters of the deep hybrid kernel extreme learning machine(DHKELM). The kernel principal component analysis(KPCA)method is used to well select the features to form an optimal feature set, which can not only express the effective information of wind power, but also avoid the appearance of redundant information, and is thus conducive to facilitating the learning and training of the DHKELM model and reducing the complexity of the model. In view of the problem that it is difficult to determine the hyperparameters of DHKELM, the improved dingo optimization algorithm(IDOA)is used to find the eight optimal hyperparameters of DHKELM and explore the original sequence feature information, so that the model can fully grasp the nonlinear relationship between numerical weather prediction(NWP)and wind power. Taking the real data of a foreign wind farm as an example, the results show that the proposed prediction model effectively improves the accuracy of wind power prediction, with the mean absolute percentage error(MAPE)0.979 3%, 2.332 1% and 3.383 2% lower than that of the dingo optimization algorithm, the differential evolution optimization algorithm and the particle swarm optimization algorithm respectively. © 2023 Xi'an Jiaotong University. All rights reserved.
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页码：66 / 77
页数：11
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  • [1] HUANG Yuhan, DING Tao, LI Yuting, Et al., Decarbonization technologies and inspirations for the development of novel power systems in the context of carbon neutrality, Proceedings of the CSEE, 41, z1, pp. 28-51, (2021)
  • [2] SHUAI Xuanyue, WANG Xiuli, WANG Zhicheng, Et al., Cooperative operation for multiple integrated energy systems considering wind power consumption by Nash bargaining convention, Journal of Xi'an Jiaotong University, 56, 3, pp. 187-196, (2022)
  • [3] WANG Lijie, DONG Lei, GAO Shuang, Wind power short-term prediction based on principal component analysis of NWP of multiple locations, Transactions of China Electrotechnical Society, 30, 5, pp. 79-84, (2015)
  • [4] YANG Jiaran, WANG Xingcheng, LUO Xiaofen, Et al., Intelligent combined prediction of wind power based on numerical weather prediction and fuzzy clustering, Acta Energiae Solaris Sinica, 38, 3, pp. 669-675, (2017)
  • [5] ZHENG Linyu, HE Hongmei, Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA, Expert Systems with Applications, 183, (2021)
  • [6] WU Dingan, ZHONG Jianwei, WANG Xinlei, Et al., Principal component analysis and long and short-term memory networks for electric load forecasting, Internet of Things Technologies, 11, 8, pp. 47-51, (2021)
  • [7] ZHANG Yunqin, CHENG Qize, JIANG Wenjie, Et al., Photovoltaic power prediction model based on EMD-PCA-LSTM, Acta Energiae Solaris Sinica, 42, 9, pp. 62-69, (2021)
  • [8] XIANG Ling, LI Jingxu, WANG Penghe, Et al., Wind speed multistep interval forecasting based on VMD-FIG and parameter-optimized GRU, Acta Energiae Solaris Sinica, 42, 10, pp. 237-242, (2021)
  • [9] XU Yaosong, WANG Dexing, Bagging-Blending multi-model fusion short-term power load forecasting based on weighted gray relational projection, Information and Control, 51, 2, pp. 188-200, (2022)
  • [10] SHENG Siqing, JIN Hang, LIU Changrong, Short-term and mid-short-term wind power forecasting based on VMD-WSGRU, Power System Technology, 46, 3, pp. 897-904, (2022)
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