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REFERENCES

  • 1
    Wilkes J, Moccia J, Dragan M. Wind in power: 2011 European statistics. Technical Report, European Wind Energy Association (EWEA), 2012.
  • 2
    Pinson P, Nielsen HA, Møller JK, Madsen H, Kariniotakis GN. Non-parametric probabilistic forecasts of wind power: required properties and evaluation. Wind Energy 2007; 10: 497516.
  • 3
    Roulston MS, Smith LA. Combining dynamical and statistical ensembles. Tellus A 2003; 55: 1630.
  • 4
    Bremnes JB. Probabilistic wind power forecasts using local quantile regression. Wind Energy 2004; 7: 4754.
  • 5
    Giebel G, Brownsword R, Kariniotakis GN, Denhard M, Draxl C. The state-of-the-art in short-term prediction of wind power – a literature overview. Technical Report, ANEMOS. plus, 2011.
  • 6
    Glahn HR, Lowry DA. The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology 1972; 11: 12031211.
  • 7
    Bremnes JB. A comparison of a few statistical models for making quantile wind power forecasts. Wind Energy 2006; 9: 311.
  • 8
    Nielsen HA, Madsen H, Nielsen TS. Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy 2006; 9: 95108.
  • 9
    Møller JK, Nielsen HA, Madsen H. Time-adaptive quantile regression. Computational Statistics & Data Analysis 2008; 52: 12921303.
  • 10
    Juban J, Fugon L, Kariniotakis GN. Probabilistic short-term wind power forecasting based on kernel density estimators, Proceedings of the 2007 European Wind Energy Conference, Milan, 2007; 710.
  • 11
    Bessa RJ, Miranda V, Botterud A, Zhou Z, Wang J. Time-adaptive quantile-copula for wind power probabilistic forecasting. Renewable Energy 2012; 40: 2939.
  • 12
    Bessa RJ, Miranda V, Botterud A. Time adaptive conditional kernel density estimation for wind power forecasting. IEEE Transactions on Sustainable Energy 2012; 3: 660669.
  • 13
    Taylor JW, McSharry PE, Buizza R. Wind power density forecasting using ensemble predictions and time series models. IEEE Transactions on Energy Conversion 2009; 24: 775782.
  • 14
    Pinson P, Madsen H. Ensemble-based probabilistic forecasting at Horns Rev. Wind Energy 2009; 12: 137155.
  • 15
    Nielsen HA, Madsen H, Nielsen TS, Badger J, Giebel G, Landberg L, Sattler K, Feddersen H. Wind power ensemble forecasting, Proceedings of the 2004 Global Wind Power Conference, Chicago, 2004; 2831.
  • 16
    Giebel G, Badger J, Landberg L. Wind power prediction using ensembles. Technical Report, Risø National Laboratory, 2005.
  • 17
    Pinson P, Kariniotakis G. Conditional prediction intervals of wind power generation. IEEE Transactions on Power Systems 2010; 25: 18451856.
  • 18
    Lange M. On the uncertainty of wind power predictions – analysis of the forecast accuracy and statistical distribution of errors. Journal of Solar Energy Engineering 2005; 127: 177184.
  • 19
    Roulston M. Using medium-range weather forcasts to improve the value of wind energy production. Renewable Energy 2003; 28: 585602.
  • 20
    Drechsel S, Mayr GJ, Messner JW, Stauffer R. Wind speeds at heights crucial for wind energy: measurements and verification of forecasts. Journal of Applied Meteorology and Climatology 2012; 51: 16021617.
  • 21
    Tobin J. Estimation of relationships for limited dependent variables. Econometrica 1958; 26: 2436.
  • 22
    Thorarinsdottir TL, Gneiting T. Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression. Journal of the Royal Statistical Society: Series A 2010; 173: 371388.
  • 23
    Wilks DS. Statistical Methods in the Atmospheric Sciences, 2nd edn. Academic Press: London, 2006.
  • 24
    Gneiting T, Raftery AE, Westveld AH, Goldman T. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 2005; 133: 10981118.
  • 25
    Wilks DS, Hamill TM. Comparison of ensemble-MOS methods using GFS reforecasts. Monthly Weather Review 2007; 135: 23792390.
  • 26
    Koenker R, Bassett Jr G. Regression quantiles. Econometrica 1978; 46: 3350.
  • 27
    Powell JL. Censored regression quantiles. Journal of Econometrics 1986; 32: 143155.
  • 28
    Portnoy S. Censored quantile regression. Journal of American Statistical Association 2003; 98: 10011012.
  • 29
    Peng L, Huang Y. Survival analysis with quantile regression models. Journal of American Statistical Association 2008; 103: 637649.
  • 30
    Lin G, He X, Portnoy S. Quantile regression with doubly censored data. Computational Statistics & Data Analysis 2012; 56: 797812.
  • 31
    Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman and Hall: London, 1994.
  • 32
    Hothorn T, Leisch F, Zeileis A, Hornik K. The design and analysis of benchmark experiments. Journal of Computational and Graphical Statistics 2005; 14: 675699.
  • 33
    Roulston MS, Kaplan DT, Hardenberg J, Smith LA. Value of the ECMWF ensemble prediction system for forecasting wind energy production, Proceedings of the 2001 European Wind Energy Conference, Copenhagen, 2001; 699702.
  • 34
    Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 2007; 102: 359378.
  • 35
    Cabezon D, Marti I, San-Isidro MJ, Perez I. Comparison of methods for power curve modeling, Proceedings to Global Windpower 2004, Chicago, 2004; 18.
  • 36
    Louka P, Galanis G, Siebert N, Kariniotakis GN, Katsafados P, Pytharoulis I, Kallos G. Improvements in wind speed forecasts for wind power prediction purposes using kalman filtering. Journal of Wind Engineering and Industrial Aerodynamics 2008; 96(12): 23482362.
  • 37
    Müller MD. Effects of model resolution and statistical postprocessing on shelter temperature and wind forecasts. Journal of Applied Meteorology and Climatology 2011; 50(8): 16271636.
  • 38
    R Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2012. Available from: http://www.R-project.org/ accessed on 2013-08-22, ISBN 3-900051-07-0.
  • 39
    Koenker R. Quantreg: quantile Regression, 2012. Available from: http://CRAN.R-project.org/package=quantreg accessed on 2013-08-23, R package version 4.91.