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.
 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.
 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.
 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.
 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
 Shuhui Li, " Wind power prediction using recurrent multilayer
perceptron neural networks", 0-7803-7989-6/03/$17.00 ┬®2003 IEEE,
PP- 2325 - 2330
 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.
 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.
 G. Sideratos and N. Hatziargyriou, "Using radial basis neural networks
to estimate wind power production", 1-4244-1298-6/07/$25.00 ┬® 2007
 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.
 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.
 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.