Open Access
E3S Web Conf.
Volume 197, 2020
75th National ATI Congress – #7 Clean Energy for all (ATI 2020)
Article Number 08016
Number of page(s) 11
Section Environmental Sustainability and Renewable Energy Sources
Published online 22 October 2020
  1. “The World Wind Energy Report 2018”. World Wind Energy Agency (WWEA), (2019). [Google Scholar]
  2. F. Chiacchio, D. D’Urso, F. Famoso, S. Brusca, J.I. Aizpurua, V. M. Catterson. “On the use of dynamic reliability for an accurate modelling of renewable power plants” in Energy, 151, 605-621 (2018). [CrossRef] [Google Scholar]
  3. F. Famoso, M. Prestipino, S. Brusca, A. Galvagno. “Designing sustainable bioenergy from residual biomass: Site allocation criteria and energy/exergy performance indicators” in Applied Energy, 274, 115315 (2020). [CrossRef] [Google Scholar]
  4. A. Galvagno, M. Prestipino, S. Maisano, F. Urbani, V. Chiodo. “Integration into a citrus juice factory of air-steam gasification and CHP system: Energy sustainability assessment” in Energy Conversion and Management, 193, (2019). [Google Scholar]
  5. W. Gądek, M. Mlonka-Mędrala, M. Prestipino, P. Evangelopoulos, S. Kalisz, W. Yang. “Gasification and pyrolysis of different biomasses in lab scale system: a comparative study” in E3S Web Conf, 10, 00024, (2016). [CrossRef] [Google Scholar]
  6. S. Brusca, F. Famoso, A. Galvagno, R. Lanzafame, S. Mauro, M. Messina. “Wind turbine wake mathematical models validation by means of wind field data” in International Journal of Applied Engineering Research, 12(24), 16068-16076, (2017). [Google Scholar]
  7. S. Brusca, F. Famoso, R. Lanzafame, A. Galvagno, S. Mauro, M. Messina. “Wind farm power forecasting: New algorithms with simplified mathematical structure” in AIP Conference Proceeding, 2191, 020028, (2019). [CrossRef] [Google Scholar]
  8. R. G. Kavasseri, K. Seetharaman. “Day-ahead wind speed forecasting using fARIMA models” in Renewable Energy, 34(5), 1388-1393, (2009). [CrossRef] [Google Scholar]
  9. L.A Landberg. “Mathematical look at physical power prediction model” in Wind Energy, 1-23, (1998). [Google Scholar]
  10. D. Elia. “Wind turbine control in computational fluid dynamics with OpenFOAM” in Wind Engineering, 41(4), (2017). [Google Scholar]
  11. S. Brusca, R. Lanzafame, F. Famoso, A. Galvagno, M. Messina, S. Mauro, M. Prestipino. “On the Wind Turbine Wake Mathematical Modelling” in Energy Procedia, 148, 202-209, (2018). [CrossRef] [Google Scholar]
  12. N. O. Jensen. “A note on wind generator interaction” in Technical Report Risø-M2411(EN), Risø National Laboratory, Roskilde, (1983). [Google Scholar]
  13. L. Tian, W. Zhu, W. Shen, Y. Song, N. Zhao. “Prediction of multi-wake problems using an improved Jensen wake model” in Renewable Energy, 102, 457-469, (2017). [CrossRef] [Google Scholar]
  14. G.W. Chang, H.J. Lu, Y.R. Chang and Y.D. Lee. “An improved neural networkbased approach for short-term wind speed and power forecast” in Renewable Energy, 105, 301-311, (2017). [CrossRef] [Google Scholar]
  15. M. Carolin Mabel, E. Fernandez. “Analysis of wind power gneration and prediction using ANN: A case study” in Renewable Energy, 33(5), 986-992, (2008). [CrossRef] [Google Scholar]

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