EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 23 (2017) E3S Web Conf., 23 (2017) 09003 References
Open Access
Issue
E3S Web Conf.
Volume 23, 2017
World Renewable Energy Congress-17
Article Number 09003
Number of page(s) 9
Section 9. Wind Energy
DOI https://doi.org/10.1051/e3sconf/20172309003
Published online 20 November 2017
  1. Lazić L., Pejanović G., Živković M., Wind forecasts for wind power generation using the Eta model, Renewable Energy, Volume 35, Issue 6, June 2010, Pages 1236-1243, ISSN 0960-1481 [Google Scholar]
  2. Zhao,X., Wang,S.X. and Li,T. (2011) Review of Evaluation Criteria and Main Methods of Wind Power Forecasting. [Google Scholar]
  3. Ma,L., Luan,S.Y., Jiang,C.W., Liu,H.L. and Zhang,Y. (2009) A Review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13, 915-920. http://dx.doi.org/10.1016/j.rser.2008.02.002 [Google Scholar]
  4. Saima,H.; Jaafar,J.; Belhaouari,S.; Jillani,T.A., Intelligent methods for weather forecasting: A review, National Postgraduate Conference (NPC), 2011 , vol., no., pp.1,6, 19-20 Sept. 2011 Energy Procedia, 12, 761-769. http://dx.doi.org/10.1016/j.egypro.2011.10.102 [Google Scholar]
  5. Ma,L., Luan,S.Y., Jiang,C.W., Liu,H.L. and Zhang,Y. (2009) A Review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13, 915-920. http://dx.doi.org/10.1016/j.rser.2008.02.002 [Google Scholar]
  6. Ma Lei., Luan Shiyan., Jiang Chuanwen., Liu. Hongling, Zhang. Yan, A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, Volume 13, Issue 4, May 2009, Pages 915-920, ISSN 1364-0321 [Google Scholar]
  7. G. Jianming Hu, Jianzhou. Wang, Guowei. Zeng, A hybrid forecasting approach applied to wind speed time series, Renewable Energy, Volume 60, December 2013, Pages 185-194, ISSN 0960-1481 [Google Scholar]
  8. Bossanyi,E.A.: Short-Term Wind Prediction Using Kalman Filters. Wind Engineering 9(1), pp. 1-8 (1985) [Google Scholar]
  9. Dambrosio,L., and D. Fortunato: One-step-ahead control of a wind-driven, synchronous generator system. Energy 24, pp. 9-20 (1999), doi:10.1016/S0360-5442(98)00067-X [CrossRef] [Google Scholar]
  10. Nogaret,E., G. Stavrakakis, J.C. Bonin, G. Kariniotakis, B. Papadias, G. Contaxis, M. Papa-dopoulos, N. Hatziargyriou, S. Papathanassiou, J. Garopoulos, E. Karagounis, J. Halliday, G. Dutton, J. Pedas-Lopes, A. Androutsos, and P. Pligoropoulos: Development and Implementation of an Advanced Control System for Medium Size Wind-Diesel Systems. Proceedings of the EWEC ‘94 in Thessaloniki, 10.-14. Okt, pp. 599-604 [Google Scholar]
  11. Tantareanu,C.: Wind Prediction in Short Term: A first step for a better wind turbine control. Nordvestjysk Folkecenter for Vedvarende Energi, October 1992, ISBN 87-7778-005-1 [Google Scholar]
  12. Dutton,A.G., G. Kariniotakis, J.A. Halliday, and E. Nogaret: Load and Wind Power Forecasting Methods for the Optimal Management of Isolated Power Systems with High Wind Penetration. Wind Engineering 23(2), pp. 69-87 (1999). [Google Scholar]
  13. Kariniotakis,G., E. Nogaret, and G. Stavrakis: Advanced Short-Term Forecasting of Wind Power Production. Proceedings of the European Wind Energy Conference held in Dublin, Ireland, October 1997, pp. 751-754, ISBN 0-9533922-0-1 . [Google Scholar]
  14. Kariniotakis,G.N., E. Nogaret, A.G. Dutton, J.A. Halliday, and A. Androutsos: Evaluation of Advanced Wind Power and Load Forecasting Meghods for the Optimal Management of IsolatedPower Systems. Proceedings of the European Wind Energy Conference, Nice, France, 1-5 March 1999, pp. 1082-1085, ISBN 1 902916 00 X . [Google Scholar]
  15. Fukuda,H., S. Tamaki, M. Nakamura, H. Nagai, F. Shijo, S. Asato, K. Onaga: The Development of a Wind Velocity Prediction Method Based on a Data-Mining Type Auto-Regressive Model. Proceedings of the European Wind Energy Conference, Copenhagen, Denmark, 2-6 June 2001, pp. 741-744, ISBN 3-936338-09-4 . [Google Scholar]
  16. Hunt,K., and G.P. Nason: Wind speed modelling and short-term prediction using wavelets. Wind Engineering 25 (1), pp. 55-61 (2001). [CrossRef] [Google Scholar]
  17. Gong Li. and Jing Shi. On comparing three artificial neural networks for wind speed forecasting. Applied Energy Volume 87, Issue 7, July 2010, pp. 2313-2320. [CrossRef] [Google Scholar]
  18. J. F. G. de Freitas, M. Niranjan, A. Gee, and A. Doucet, Sequential Monte Carlo methods to train neural network models, Neural Comput.,vol. 12, no. 4, pp. 955–993, Apr. 2000. [CrossRef] [PubMed] [Google Scholar]
  19. N. de Freitas, C. Andrieu, P. Højen-Sørensen, M. Niranjan, and A. Gee, Sequential Monte Carlo methods for neural networks, in SequentialMonte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. New York, USA: Springer-Verlag, 2001, pp. 359–380. [CrossRef] [Google Scholar]
  20. T. Kurihara, Y. Nakada, K. Yosui, and T. Matsumoto, Bayesian online learning: A sequential Monte Carlo with importance resampling, in Proc. IEEE Signal Process. Soc. Workshop Neural Netw. Signal Process.,Sep. 2001, pp. 163–172. [Google Scholar]
  21. Yohei. Nakada and Takashi. Matsumoto, Online Bayesian Learning WithNatural Sequential Prior Distribution, IEEE transactions on neural networks and learning systems, vol. 25, no. 1, january 2014. [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.