• Barthelmie R, Murray F, Pryor S. 2008. The economic benefit of short-term forecasting for wind energy in the UK electricity market. Energy Policy 36(5): 16871696.
  • Brown BG, Katz RW, Murphy AH. 1984. Time series models to simulate and forecast wind speed and wind power. Journal of Applied Meteorology 23(8): 11841195.
  • Cadenas E, Rivera W. 2010. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renewable Energy 35(12): 27322738.
  • Cheng WYY, Steenburgh WJ. 2007. Strengths and weaknesses of MOS, running-mean bias removal, and Kalman filter techniques for improving model forecasts over the western United States. Weather and Forecasting 22(6): 13041318.
  • COSMO. 2010. Cosmo Public area. The Consortium for Small-scale Modeling (COSMO). (accessed 1 July 2011).
  • Costa A, Crespo A, Navarro J, Lizcano G, Madsen H, Feitosa E. 2008. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12(6): 17251744.
  • Crochet P. 2004. Adaptive Kalman filtering of 2-metre temperature and 10-metre wind-speed forecasts in Iceland. Meteorological Applications 11(2): 173187.
  • EirGrid. 2010. EirGrid Plc. Annual renewable report 2010. Renewable Report 2010.pdf (accessed 1 July 2011).
  • Galanis G, Anadranistakis M. 2002. A one-dimensional Kalman filter for the correction of near surface temperature forecasts. Meteorological Applications 9(4): 437441.
  • Giebel G. 2003. The state-of-the-art in short-term prediction of wind power. Project ANEMOS. (accessed 11 November 2010).
  • Glahn HR, Lowry DA. 1972. The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology 11(8): 12031211.
  • Kalman R. 1960. A new approach to linear filtering and prediction problems. ASME Journal of Basic Engineering 82: 3545.
  • Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z. 2009. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13(4): 915920.
  • Li G, Shi J. 2010. On comparing three artificial neural networks for wind speed forecasting. Applied Energy 87(7): 23132320.
  • Louka P, Galanis G, Siebert N, Kariniotakis G, Katsafados P, Pytharoulis I, Kallos G. 2008. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Journal of Wind Engineering and Industrial Aerodynamics 96(12): 23482362.
  • Nielsen HA, Nielsen TS, Madsen H, Pindado M, Marti I. 2007. Optimal combination of wind power forecasts. Wind Energy 10(5): 471482.
  • R Development Core Team. 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna. (accessed August 2011).
  • Ripley BD. 1996. Pattern Recognition and Neural Networks. Cambridge University Press: Cambridge, UK.
  • Salcedo-Sanz S, Pérez-Bellido A, Ortiz-García E, Portilla-Figueras A, Prieto L, Paredes D. 2009a. Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction. Renewable Energy 34(6): 14511457.
  • Salcedo-Sanz S, Pérez-Bellido A, Ortiz-García E, Portilla-Figueras A, Prieto L, Correoso F. 2009b. Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks. Neurocomputing 72(4–6): 13361341.
  • Sfetsos A. 2000. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21(1): 2335.
  • Stensrud DJ, Yussouf N. 2005. Bias-corrected short-range ensemble forecasts of near surface variables. Meteorological Applications 12(3): 217.
  • Steppeler J, Doms G, Schattler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G. 2003. Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorology and Atmospheric Physics 82(1–4): 7596, DOI: 10.1007/s00703-001-0592-9.
  • Sweeney C, Lynch P. 2011. Adaptive post-processing of short-term wind forecasts for energy applications. Wind Energy 14(3): 317325, DOI: 10.1002/we.420.
  • Torres JL, García A, De Blas M, De Francisco A. 2005. Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar Energy 79(1): 6577.
  • Untch A, Miller M, Hortal M, Buizza R, Janssen P. 2006. Towards a global meso-scale model: the high-resolution system T799L91 and T399L62 EPS. ECMWF Newsletter 108: 613.
  • Venables WN, Ripley BD. 2002. Modern Applied Statistics with S, 4th edn. Springer: New York, NY.