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All issues Volume 64 (2018) E3S Web Conf., 64 (2018) 06002 References
Open Access
Issue
E3S Web Conf.
Volume 64, 2018
2018 3rd International Conference on Power and Renewable Energy
Article Number 06002
Number of page(s) 6
Section Photovoltaic Systems and Power Generation Technologies
DOI https://doi.org/10.1051/e3sconf/20186406002
Published online 27 November 2018
  1. K. Rohrig et al. , “Windenergie Report Deutschland 2016,” Kassel, 2017 [Google Scholar]
  2. T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, and R. J. Hyndman, “Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond,” Int. J. Forecast., vol. 32, no. 3, pp. 896–913, 2016. [Google Scholar]
  3. P. Pinson, “Estimation of the uncertainty in wind power forecasting,” Ecole nationale superieure des mines, Paris, 2006. [Google Scholar]
  4. G. Aquila, P. Rotela Junior, E. de Oliveira Pamplona, and A. R. de Queiroz, “Wind power feasibility analysis under uncertainty in the Brazilian electricity market,” Energy Econ., vol. 65, pp. 127–136, 2017. [CrossRef] [Google Scholar]
  5. L. Castro-Santos and V. Diaz-Casas, “Sensitivity analysis of floating offshore wind farms,” Energy Convers. Manag., vol. 101, pp. 271–277, 2015. [Google Scholar]
  6. A. Bossavy, R. Girard, and G. Kariniotakis, “Sensitivity analysis in the technical potential assessment of onshore wind and ground solar photovoltaic power resources at regional scale,” Applied Energy, vol. 182, pp. 145–153, 2016. [Google Scholar]
  7. N. E. Mohammad Rozali, S. R. Wan Alwi, and Z. A. Manan, “Sensitivity analysis of hybrid power systems using Power Pinch Analysis considering Feed-in Tariff,” Energy, vol. 116, pp. 1260–1268, 2016. [CrossRef] [Google Scholar]
  8. M. Song, K. Chen, X. Zhang, and J. Wang, “Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction,” Renewable Energy, vol. 85, pp. 57–65, 2016. [CrossRef] [Google Scholar]
  9. F. Monforti and I. Gonzalez-Aparicio, “Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union,” Applied Energy, vol. 206, pp. 439–450, 2017. [Google Scholar]
  10. D. G. Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni and Michaela Saisana and Stefano Tarantola, Global Sensitivity Analysis. The Primer. Wiley, 2008. [Google Scholar]
  11. C. Crambes, A. Gannoun, and Y. Henchiri, “Weak consistency of the Support Vector Machine Quantile Regression approach when covariates are functions,” Stat. Probab. Lett., vol. 81, no. 12, pp. 1847–1858, 2011. [Google Scholar]
  12. C. Crambes, A. Gannoun, and Y. Henchiri, “Support vector machine quantile regression approach for functional data: Simulation and application studies,” J. Multivar. Anal., vol. 121, pp. 50–68, 2013. [Google Scholar]
  13. B. Kriegler and R. Berk, “Boosting the Quantile Distribution: A Cost-Sensitive Statistical Learning Procedure,” Los Angeles, 2007. [Google Scholar]
  14. A. Cannon, “Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes,” 2017. [Google Scholar]
  15. A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola, “Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index,” Comput. Phys. Commun., vol. 181, no. 2, pp. 259–270, 2010. [Google Scholar]
  16. J. Dobschinski, “How good is my forecast? Comparability of wind power forecast erros,” in Workshop on Large Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, 2014, vol. 13, pp. 1–5. [Google Scholar]
  17. J. Li et al., “Feature Selection: A Data Perspective,” p. 45, Jan. 2016. [Google Scholar]
  18. A. Gensler, S. Vogt, and B. Sick, “Metaverication of Uncertainty Representations and Assessment Techniques for Power Forecasting Algorithms including Ensembles,” unpublished. [Google Scholar]
  19. J. Xie and T. Hong, “Variable Selection Methods for Probabilistic Load Forecasting: Empirical Evidence from Seven States of the United States,” IEEE Trans. Smart Grid, pp. 1–1, 2017. [Google Scholar]
  20. C. Hübler, C. G. Gebhardt, and R. Rolfes, “Hierarchical four-step global sensitivity analysis of offshore wind turbines based on aeroelastic time domain simulations,” Renewable Energy, vol. 111, pp. 878–891, 2017. [CrossRef] [Google Scholar]
  21. S. Kohler, A.-C. Agricola, and H. Seidl, “dena-Netzstudie II,” Berlin, 2010. [Google Scholar]

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