EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 309 (2020) MATEC Web Conf., 309 (2020) 05011 References
Open Access
Issue
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 05011
Number of page(s) 12
Section Modelling and Simulation
DOI https://doi.org/10.1051/matecconf/202030905011
Published online 04 March 2020
  1. Hui L, Mi X, Li Y. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM [J]. Energy Conversion & Management, 2018, 159(54-64. [Google Scholar]
  2. Jiang Zhao-Yu, Jia Qing-Shan et.al. A Review of Multi-temporal-and-spatial-scale Wind Power Forecasting Method [J]. Acta Automatica Sinica, 2019, 45(1): 51–71. [Google Scholar]
  3. Khoddayar M, Wang J. Spatio-temporal Graph Deep Neural Network for Short-term Wind Speed Forecasting [J]. IEEE Transactions on Sustainable Energy, 2018, PP(99): 1-. [Google Scholar]
  4. Yu C, Li Y, Zhang M. Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting [J]. Energy Conversion and Management, 2017, 147(75-85. [Google Scholar]
  5. L.Ngkvist M, Karlsson L, Loutei A. A review of unsupervised feature learning and deep learning for time-series modeling [J]. Pattern Recognition Letters, 2014, 42(11–24. [Google Scholar]
  6. Da L, Niu D, Hui W, et al. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm [J]. Renewable Energy, 2014, 62(3): 592–7. [CrossRef] [Google Scholar]
  7. Lei J, Liu C, Jiang D. Fault diagnosis of wind turbine based on Long Short-term memory networks [J]. Renewable Energy, 2019, 133(422–32. [CrossRef] [Google Scholar]
  8. Chen J, Zeng G-Q, Zhou W, et al. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization [J]. Energy Conversion and Management, 2018, 165(681–95. [CrossRef] [Google Scholar]
  9. Hu Y-L, Chen L. A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm [J]. Energy Conversion and Management, 2018, 173(123–42. [CrossRef] [Google Scholar]
  10. Li Y, Wu H, Liu H. Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction [J]. Energy Conversion and Management, 2018, 167(203–19. [CrossRef] [Google Scholar]
  11. Zhu Q-M, Li H-Y, Wang Z-Q, et al. Ultra short term prediction of wind power generation based on short-term memory network [J]. Power System Technology, 2017, 41(12): 3797–802. [Google Scholar]
  12. Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J]. Neural Netw, 2005, 18(5): 602–10. [CrossRef] [Google Scholar]
  13. Zhen Y, Yang H, Feng Y, et al. A convolution BiLSTM neural network model for Chinese event extraction [M]. Natural Language Understanding and Intelligent Applications. Springer. 2016: 275–87. [Google Scholar]
  14. Wang L-J, Dong Lei, et al. Short-term Power Prediction of a Wind Farm Based on Wavelet Analysis [J]. Proceeding of the CSEE, 2009, 29(28): 30–3. [Google Scholar]
  15. Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1989,: 674–93. [Google Scholar]
  16. Mi X-W, Liu H, LiI Y-F. Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm [J]. Energy Conversion and Management, 2017, 151(709–22. [CrossRef] [Google Scholar]
  17. Hocheriter S. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions [J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998, 6(02): 10. [Google Scholar]
  18. Graves A. Long Short-Term Memory [J]. Neural Computation, 1997, 9(8): 1735–80. [CrossRef] [Google Scholar]
  19. Bai Jing, Li Fei, Ji D-H. Attention based Chinese microblog position detection model of bilstm-cnn [J]. Computer application and software, 2018, 35(3): 266–74. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.