Public Service of Wallonie, GREDOR Project, https://gredor.be/Date of access: 10th of March 2014.
Kwon Kim, Young-Jun Seo, Kyong-Seob Moon and Young-Mi Lee. Real-time Wind Power Prediction System Based on Smart-Grid in Jeju, Korea. Journal of Interantional Council on Electrical Engineering Vol. 2, No. 2 (2012); pp. 194-200.
C.W. Poter and M. Negnevitsky. Very short-term wind forecasting for Tasmanian power generation. IEEE Transactions on Power Systems, vol. 21, no. 2 (2006); pp. 965-972.
Yu Jiang, Zhe Song, Andrew Kusiak. Very short term wind speed forecasting with Bayesian structural break model. Renewable Energy 50 (2013); pp.637-647.
G. Giebel, R. Brownsword and G. Kariniotakis. The state of the art in short term prediction of wind power: A Literature Overview. The European Commission under the 5th (EC) RTD Framework Programme (1998-2002); pp.1-36.
X.L. Ling Chen. Comparison between ARIMA and ANN modelsused in short term wind speed forecasting. Power and Energy Engineering Conference (APPEEC), 25-28 March (2011); pp.1-4.
T.M.H. El-Fouly, E.F. El-Saadany and M.M.A. Salama. One day ahead prediction of wind speed using annual trends. IEEE Power Engineering Society, 18-22 June (2006); pp. 7-12.
Xiaoqian, Bind Dong, Le Xie, Latanya Sweeney. Adaptive Gaussian Process for short term wind speed forecasting. Proceedings of the 2010 conference on ECAI 2010; pp 661-666.
Rajesh G. Kavasseri and Krithika Seetharaman. Day Ahead Wind Speed Forecasting using f-ARIMA models. Renewable Energy, Vol. 35, No. 5, May 2009; pp.1388-1393.
T.G. Barbounis, J.B. Theocharis, M.C. Alexiadis and P.D. Dokopoulos. Long-Term Wind Speed and Power Forecasting using Local Recurrent Neural Network Models. IEEE Transactions on Energy Conversion, Vol. 21, No. 1, March 2006.
H.G. Beyer, T. Degner, J. Hausmann, M. Hoffmann and P. Rujan. Short Term Prediction of Wind Speed and Power Output of a Wind Turbine with Neural Networks. Proceedings of the EWEC '94 in Thessaloniki, 10-14 Oct.; pp. 349-352.
J.O. Tande and L. Landberg. A 10 sec. Forecast of Wind Turbine Output with Neural Networks. Proceedings of the EWEC '93 in Travemunde, 8-12 March; pp. 774-777.
A. More and M.C. Deo. Forecasting with neural networks. Marine Structures 16; pp.35-49.
J.L. Torres, A. Garcia, M. De-Blas and A. De-Francisco. Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar Energy 79 (2005); pp. 65-77.
B.G. Brown, R.W. Gatz and A.H. Murphy. Time Series Models to simulate and Forecast Wind Speed and Wind Power. Journal of Climate and Applied Meteorology 23(8) (1984); pp. 1184-1195.
L. Kamal and Y.Z. Jafri. Time Series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Solar Energy 61(1) (1997); pp.23-32.
J. Dupacova, N.Growe-Kuska and Werner Romisch. Scenario reduction in stochastic programming: An approach using probability metrics. Mathematical Programming, Vol. 95, No. 3 (2003); pp. 493-511.
N.Growe-Kuska, N. Heitsch and Werner Romisch. Scenario Reduction and Scenario Tree Construction for Power Management Problems. Contribution to the IEEE Power Tech Conference, Bologne 2003.
H. Brand, E. Thorin, C. Weber. Scenario Reduction algorithm and creation of multistage scenario trees. Discussion paper no.7 of the EU-project Oscogen, Stuttgart 2002.
R. Barth, L. Soder, C. Weber, H. Brand, D. J. Swider. Methodology of the scenario tree tool. Institute of Energy and the Rational Use of Energy, University of Stuttgart, January 2006.
Q. Gemine, D. Ernst, Q. Louveaux and B. Cornelusse. Relaxations for multi-period optimal power flow problems with discrete decision variables. Proceedings of the IREP 2013 Symposium in Crete Greece, 25-30 August.
K. P. Burnham and D. R. Anderson. Model selection and multimodel inference: a practical information-theoretic approach; Springer: New York, 2002.
