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Neural Networks for Short Term Wind Speed Prediction
Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm.
Digital Article Identifier (DAI):


[1] X.Wang, G. Sideratos, N. Hatziargyriou and L. H. Tsoukalas, " Wind speed forecasting for power system operational planning" , 8th Intl. Conf. on Probabilistic Methods Applied to Power Systems, Iowa State University, Iowa, September 12-16, 2004, pp 470 - 474.
[2] Anthanasios Sfetsos and Costas Siriopoulos, "Time series forecasting of averaged data with efficient use of information", IEEE Trans on Systems, Man and Cybernatics- Part A: Systems and Humans", Digital object identifier- 10.1109/TMSCA2005.851133.
[3] Ioannis.G.Damousis, Mians C. Alexadis, John B Theocharis and Petros,S.Dokopoulos ,"A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation", IEEE Trans. on Energy Conversions, Vol. 19, No.2, PP 352 - 361, June 2004.
[4] Shuhui Li, Donald C Wunsch, Edgar O-Hair and Michael G Giesselmann, " Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation", Journal of Solar Energy Engineering, Vol. 123, pp 327 - 332, November 2001
[5] Shuhui Li, " Wind power prediction using recurrent multilayer perceptron neural networks", 0-7803-7989-6/03/$17.00 ┬®2003 IEEE, PP- 2325 - 2330
[6] Henrique Steinhertz Hippert, CarlosEduardo Pedreira and Reinaldo Castro Souza, "Neural networks for short term load forecasting: A review and evaluation", IEEET Trans. on Power Systems, Vol 16. No.1 pp 44- 55, February 2001.
[7] T.G.Barbounis and J B Theocharis, " Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlation", Proceedings of The Intl. Joint Conf. on Neural Networks, Canada, July 31 - August 4, 2005, pp 2711 - 2716.
[8] G. Sideratos and N. Hatziargyriou, "Using radial basis neural networks to estimate wind power production", 1-4244-1298-6/07/$25.00 ┬® 2007 IEEE.
[9] Enrique Romero and Daniel Toppo, "Comparing support vector machines and feed forward neural networks with similar hidden layer weights", IEEE Trans. on Neural Networks, Vol 18, no 3. May 2007, pp 959 - 963.
[10] T.G. Barbounis, J B Theocharis, Minas. C. Alexadis and Petros. S. Dokopoulos, " Long term wind speed and power forecasting using local recurrent neural network models", IEEE Trans. on Energy Conversion, Vol 21, no. 1, March 2006, pp 273 - 284.
[11] Alireza Khotanzad, Reza Afkhami Rohani and Dominic Maratukulam, "ANNSTLF - artificial neural network short-term load forecaster - generation three." IEEE Trans. on Power Systems, Vol 13, No.4, November 1998, pp 1413-1422.
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