Wind power generated by wind has non-schedule nature due to stochastic nature
of meteorological variable. Hence energy business and control of wind power
generation requires prediction of wind speed (WS) from few seconds to different
time steps in advance. To deal with prediction shortcomings, various WS
prediction methods have been used. Predictive data mining offers variety of
methods for WS predictions where artificial neural network (ANN) is one of the
reliable and accurate methods. It is observed from the result of this study
that ANN gives better accuracy in comparison conventional model. The accuracy
of WS prediction models is found to be dependent on input parameters and
architecture type algorithms utilized. So the selection of most relevant input
parameters is important research area in WS predicton field. The objective of
the paper is twofold: first extensive review of ANN for wind power and WS
prediction is carried out. Discussion and analysis of feature selection using
Relief Algorithm (RA) in WS prediction are considered for different Indian
sites. RA identify atmospheric pressure, solar radiation and relative humidity
are relevant input variables. Based on relevant input variables Cascade ANN
model is developed and prediction accuracy is evaluated. It is found that root
mean square error (RMSE) for comparison between predicted and measured WS for
training and testing wind speed are found to be 1.44 m/s and 1.49 m/s
respectively. The developed cascade ANN model can be used to predict wind speed
for sites where there are not WS measuring instruments are installed in India.

通过人工神经网络方法进行风力发电和风速预测的广泛综述，并考虑了不同印度站点的特征选择、级联人工神经网络模型和预测准确性评估，以解决风速预测中最相关输入参数的选择问题。