EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00116 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00116
Number of page(s) 10
DOI https://doi.org/10.1051/e3sconf/202568000116
Published online 19 December 2025
  1. International Energy Agency, World Energy Outlook 2023. in World Energy Outlook. OECD, 2023. doi: 10.1787/827374a6-en. [Google Scholar]
  2. P. Veers et al., “Grand challenges in the science of wind energy,” Science, vol. 366, no. 6464, p. eaau2027, Oct. 2019, doi: 10.1126/science.aau2027. [CrossRef] [Google Scholar]
  3. S. Tumse, M. Bilgili, A. Yildirim, and B. Sahin, “Comparative Analysis of Global Onshore and Offshore Wind Energy Characteristics and Potentials,” Sustainability, vol. 16, no. 15, p. 6614, Aug. 2024, doi: 10.3390/su16156614. [CrossRef] [Google Scholar]
  4. Gondia Sokhna Seck, Bishal Parajuli, and X. G. Casals, “IRENA World energy transitions outlook 2023 volume 2,” 2023, doi: 10.13140/RG.2.2.10445.65768. [Google Scholar]
  5. L. J. Vermeer, J. N. Sørensen, and A. Crespo, “Wind turbine wake aerodynamics,” Progress in Aerospace Sciences, vol. 39, no. 6–7, pp. 467–510, Aug. 2003, doi: 10.1016/S0376-0421(03)00078-2. [Google Scholar]
  6. R. J. A. M. Stevens and C. Meneveau, “Flow Structure and Turbulence in Wind Farms,” Annu. Rev. Fluid Mech., vol. 49, no. 1, pp. 311–339, Jan. 2017, doi: 10.1146/annurev-fluid-010816-060206. [Google Scholar]
  7. M. F. Howland, S. K. Lele, and J. O. Dabiri, “Wind farm power optimization through wake steering,” Proc. Natl. Acad. Sci. U.S.A., vol. 116, no. 29, pp. 14495–14500, July 2019, doi: 10.1073/pnas.1903680116. [Google Scholar]
  8. P. Veers et al., “Grand Challenges: wind energy research needs for a global energy transition,” Wind Energ. Sci., vol. 7, no. 6, pp. 2491–2496, Dec. 2022, doi: 10.5194/wes-7-2491-2022. [Google Scholar]
  9. M. Bastankhah and F. Porté-Agel, “A new analytical model for wind-turbine wakes,” Renewable Energy, vol. 70, pp. 116–123, Oct. 2014, doi: 10.1016/j.renene.2014.01.002. [Google Scholar]
  10. T. Göçmen, P. V. D. Laan, P.-E. Réthoré, A. P. Diaz, G. Chr. Larsen, and S. Ott, “Wind turbine wake models developed at the technical university of Denmark: A review,” Renewable and Sustainable Energy Reviews, vol. 60, pp. 752–769, July 2016, doi: 10.1016/j.rser.2016.01.113. [Google Scholar]
  11. L. Wang et al., “Wind turbine wakes modeling and applications: Past, present, and future,” Ocean Engineering, vol. 309, p. 118508, Oct. 2024, doi: 10.1016/j.oceaneng.2024.118508. [Google Scholar]
  12. Niels Otto Jensen, “A note on wind generator interaction,” Risø National Laboratory, Roskilde, Denmark, Technical Report Risø-M-2411, 1983. [Online]. Available: https://orbit.dtu.dk/en/publications/a-note-on-wind-generator-interaction [Google Scholar]
  13. F. Blondel, “Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model,” Wind Energ. Sci., vol. 8, no. 2, pp. 141–147, Feb. 2023, doi: 10.5194/wes-8-141-2023. [Google Scholar]
  14. M. Calaf, C. Meneveau, and J. Meyers, “Large eddy simulation study of fully developed wind-turbine array boundary layers,” Physics of Fluids, vol. 22, no. 1, p. 015110, Jan. 2010, doi: 10.1063/1.3291077. [Google Scholar]
  15. Z. Liu, H. Guo, Y. Zhang, and Z. Zuo, “A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges,” Energies, vol. 18, no. 2, p. 350, Jan. 2025, doi: 10.3390/en18020350. [Google Scholar]
  16. C. Yang, Z. Qian, Y. Pei, and L. Wei, “A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems,” Energies, vol. 11, no. 8, p. 2142, Aug. 2018, doi: 10.3390/en11082142. [Google Scholar]
  17. J. A. Aird, E. W. Quon, R. J. Barthelmie, M. Debnath, P. Doubrawa, and S. C. Pryor, “Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain,” Remote Sensing, vol. 13, no. 21, p. 4438, Nov. 2021, doi: 10.3390/rs13214438. [Google Scholar]
  18. Z.-H. Liu, C.-T. Wang, H.-L. Wei, B. Zeng, M. Li, and X.-P. Song, “A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data,” Expert Systems with Applications, vol. 247, p. 123237, Aug. 2024, doi: 10.1016/j.eswa.2024.123237. [Google Scholar]
  19. X. Xiong, X. Guo, P. Zeng, R. Zou, and X. Wang, “A Short-Term Wind Power Forecast Method via XGBoost Hyper-Parameters Optimization,” Front. Energy Res., vol. 10, p. 905155, May 2022, doi: 10.3389/fenrg.2022.905155. [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.