Open Access
Issue
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
Article Number 03005
Number of page(s) 8
Section Wind Turbine and Energy Systems
DOI https://doi.org/10.1051/e3sconf/202454003005
Published online 21 June 2024
  1. Nezhad, M. M., Heydari, A., Pirshayan, E., Groppi, D., & Garcia, D. A. (2021). A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method. Renewable Energy, 179, 2198–2211. [CrossRef] [Google Scholar]
  2. Kosovic, B., Haupt, S. E., Adriaansen, D., Alessandrini, S., Wiener, G., Delle Monache, L.,... & Prestopnik, P. (2020). A comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction. Energies, 13(6), 1372 [CrossRef] [Google Scholar]
  3. Neshat, M., Sergiienko, N. Y., Amini, E., Majidi Nezhad, M., Astiaso Garcia, D., Alexander, B., & Wagner, M. (2020). A new bi-level optimisation framework for optimising a multi-mode wave energy converter design: A case study for the Marettimo Island, Mediterranean Sea. Energies, 13(20), 5498 [CrossRef] [Google Scholar]
  4. Doubrawa, P., Barthelmie, R. J., Pryor, S. C., Hasager, C. B., Badger, M., & Karagali, I. (2015). Satellite winds as a tool for offshore wind resource assessment: The Great Lakes Wind Atlas. Remote Sensing of Environment, 168, 349–359. [CrossRef] [Google Scholar]
  5. Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1part2), 547–556. [CrossRef] [Google Scholar]
  6. Christiansen, M. B., & Hasager, C. B. (2006). Using airborne and satellite SAR for wake mapping offshore. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 9(5), 437–455. [Google Scholar]
  7. Soman, S. S., Zareipour, H., Malik, O., & Mandal, P. (2010, September). A review of wind power and wind speed forecasting methods with different time horizons. In North American power symposium 2010 (pp. 1–8). IEEE. [Google Scholar]
  8. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51–67. [CrossRef] [Google Scholar]
  9. Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1, 125–151. [CrossRef] [Google Scholar]
  10. Thompson, G., Field, P. R., Rasmussen, R. M., & Hall, W. D. (2008). Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Monthly weather review, 136(12), 5095–5115. [CrossRef] [Google Scholar]
  11. Bernstein, B. C., McDonough, F., Politovich, M. K., Brown, B. G., Ratvasky, T. P., Miller, D. R.,... & Cunning, G. (2005). Current icing potential: Algorithm description and comparison with aircraft observations. Journal of Applied Meteorology and Climatology, 44(7), 969–986. [CrossRef] [Google Scholar]
  12. Mahoney, W. P., Parks, K., Wiener, G., Liu, Y., Myers, W. L., Sun, J.,... & Haupt, S. E. (2012). A wind power forecasting system to optimize grid integration. IEEE Transactions on Sustainable Energy, 3(4), 670–682. [CrossRef] [Google Scholar]
  13. Brearley B.J., Bose K.R., Senthil K., Ayyappan G., (2022), “KNN APPROACHES BY USING BALL TREE SEARCHING ALGORITHM WITH MINKOWSKI DISTANCE FUNCTION ON SMART GRID DATA”,Indian Journal of Computer Science and Engineering,Vol.13,no.4,pp.1210–1226. doi:10.21817/indjcse/2022/v13i4/221304179 [Google Scholar]
  14. Sunder Selwyn T., Hemalatha S., (2020), “Condition monitoring and vibration analysis of asynchronous generator of the wind turbine at high uncertain windy regions in India”, Materials Today: Proceedings, Vol.46, pp.3639–3643. doi:10.1016/j.matpr.2021.01.656 [CrossRef] [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.