SHORT-TERM WIND SPEED PREDICTION BASED ON PAM-SSD-LSTM

被引：0
|
作者：
Zhao X. [1 ]
Chen C. [1 ]
Bi G. [1 ]
Chen S. [1 ]
机构：
[1] School of Electric Power Engineering, Kunming University of Science and Technology, Kunming
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 01期
关键词：
LSTM neural network; partitioning around medoids; singular spectrum decomposition; wind speed short-term prediction;
D O I：
10.19912/j.0254-0096.tynxb.2021-0900
中图分类号：
学科分类号：
摘要：
Wind speed has characteristics of non- linearity，non- stationarity，intermittent and randomness. In order to improve the accuracy of short-term wind speed prediction，this paper proposes a combination prediction model based on PAM clustering，singular spectrum decomposition（SSD）and LSTM neural network to predict short- term wind speed to solve the problems mentioned above. Firstly，PAM algorithm is used to perform similar daily clustering on the original wind speed data to improve the learning efficiency of the neural network. Secondly，with the advantage of suppressing model aliasing and false components，SSD is used to decompose the wind speed series and extract multi- scale regular pattern. Finally，with a strong ability to capture the fluctuation law of long- term dependent series，LSTM neural network is used to predict the decomposed wind speed components. The prediction results of each component are accumulated to obtain the wind speed prediction results. Experiment results show that the combined prediction model based on PAM-SSD-LSTM can effectively improve the accuracy of short-term wind speed prediction. © 2023 Science Press. All rights reserved.
引用
收藏
页码：281 / 288
页数：7
相关论文
共 21 条
  • [1] WANG W S, WANG Z，, DONG C，, Et al., Current status and error analysis of short- term wind power forecasting technology in China［J］, Automation of electric power systems, 45, 1, pp. 17-27, (2021)
  • [2] JIN J, YU M, Et al., Short- term wind speed prediction based on EMD optimized by Lorenz equation ［J］, Acta energiae solaris sinica, 42, 6, pp. 342-348, (2021)
  • [3] ZHU X Y, ZHANG J H, FU S P，, Et al., Short-term wind speed prediction model based on EEMD and SVM［J］, Journal of North China Electric Power University（natural science edition）, 40, 5, pp. 60-64, (2013)
  • [4] WEI X, SHI Q，, FU W X，, Et al., Short-term wind speed prediction with CEEMDAN sample entropy and SVR［J］, Hydropower energy science, 38, 11, pp. 207-210, (2020)
  • [5] WANG J，, LI X，, ZHOU X D, Et al., Ultra-short-term wind speed prediction based on VMD- LSTM［J］, Power system protection and control, 48, 11, pp. 45-52, (2020)
  • [6] YE R L, GUO Z Z，, LIU R Y，, Et al., Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network［J］, Transactions of China Electrotechnical Society, 32, 21, pp. 103-111, (2017)
  • [7] TANG G J, LI N N, WANG X L., Fault feature extraction method for gears based on ISSD and SVD［J］, China mechanical engineering, 31, 24, pp. 2988-2996, (2020)
  • [8] WANG Z J，, Et al., Application of improved singular spectrum decomposition method for composite fault diagnosis of gear boxes［J］, Sensors, 18, 11, pp. 1-29, (2018)
  • [9] LIU J H, WANG J，, YANG H C，, Et al., Power load clustering analysis based on ACO- PAM comprehensive algorithm［J］, Journal of electric power science and technology, 26, 4, pp. 94-99, (2011)
  • [10] YANG L, WANG J L，, Et al., Research on recurrent neural network［J］, Journal of computer applications, 38, S2, pp. 1-6, (2018)
123