EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 185 (2020) E3S Web Conf., 185 (2020) 01052 References
Open Access
Issue
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
Article Number 01052
Number of page(s) 7
Section Energy Engineering and Power System
DOI https://doi.org/10.1051/e3sconf/202018501052
Published online 01 September 2020
  1. Antonanzas J, Osorio N, Escobar R, et al. Review of photovoltaic power forecasting[J]. Solar Energy, 2016, 136: 78–111. [CrossRef] [Google Scholar]
  2. W. Zhang, Z. Qu, K. Zhang, W. Mao, Y. Ma, X. Fan, “A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting”., Energy Convers Manage., vol. 136, pp. 439–451, Mar. 2017. [CrossRef] [Google Scholar]
  3. J. Zhao, Z.-H. Guo, Z.-Y. Su, Z.-Y. Zhao, X. Xiao, F. Liu, “An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed”., Appl Energy, vol. 162, pp. 808–826, Jan. 2016. [Google Scholar]
  4. M. Lydia, S. S. Kumar, A. I. Selvakumar, G. E. P. Kumar, “Linear and non-linear autoregressive models for short-term wind speed forecasting”., Energy Convers Manage., vol. 112, pp. 115–124, Mar. 2016. [CrossRef] [Google Scholar]
  5. E. Erdem, J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and direction”, Appl. Energy, vol. 88, no. 4, pp. 1405–1414, 2011. [Google Scholar]
  6. R. G. Kavasseri, K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models”, Renew. Energy, vol. 34, no. 5, pp. 1388–1393, 2009. [Google Scholar]
  7. Z. Song, Y. Jiang, Z. Zhang, “Short-term wind speed forecasting with Markov-switching model”, Appl. Energy, vol. 130, no. 3, pp. 103–112, 2014. [Google Scholar]
  8. S. Baran, “Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components”, Comput. Statist. Data Anal., vol. 75, no. 3, pp. 227–238, 2014 [CrossRef] [Google Scholar]
  9. H. Mori, S. Okura, “An ANN-based method for wind speed forecasting with S-transform”, Proc. IEEE Region 10 Conf., pp. 642–645, Nov. 2016. [Google Scholar]
  10. P. Jiang, Y. Wang, J. Wang, “Short-term wind speed forecasting using a hybrid model”, Energy, vol. 119, pp. 561–577, Jan. 2017. [CrossRef] [Google Scholar]
  11. D. Bai, J. He, X. Wang, “Combination model for forecasting wind speed forecasting wind speed based on adaptive PSO-ELM”, Acta Energiae Solaris Sinica, vol. 36, no. 3, pp. 792–797, 2015. [Google Scholar]
  12. Z. Yihui et al., “A hybrid short-term wind speed forecasting model based on ensemble empirical mode decomposition and improved extreme learning machine”, Power Syst. Protection Control, vol. 10, no. 42, pp. 29–34, Dec. 2017. [Google Scholar]
  13. X. Luo, J. Sun, L. Wang, W. Wang, W. Zhao, J. Wu, J.-H. Wang, Z. Zhang, “Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy”, IEEE Trans. Ind. Informat., vol. 14, no. 11, pp. 4963–4971, Nov. 2018. [CrossRef] [Google Scholar]
  14. Z. Sun, H. Sun, J. Zhang, “Multistep wind speed and wind power prediction based on a predictive deep belief network and an optimized random forest”, Math. Problems Eng., vol. 7, no. 10, pp. 115, Jul. 2018. [Google Scholar]
  15. Urquhart B, Ghonima M, Nguyen D, et al. Sky imaging systems for short-term forecasting[J]. Solar Energy Forecasting and Resource Assessment, 2013: 195–232. [Google Scholar]
  16. Chu Y, Urquhart B, Gohari S M I, et al. Short-term reforecasting of power output from a 48 MWe solar PV plant[J]. Solar Energy, 2015, 112: 68–77. [CrossRef] [Google Scholar]
  17. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234–241. [Google Scholar]
  18. Andreas, A.; Stoffel, T.; (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS); Golden, Colorado (Data); NREL Report No. DA-5500-56488.http://dx.doi.org/10.5439/1052221 [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.