@Article{iasc.2020.013941,
AUTHOR = {Zhenhua Guo, Lixin Zhang, Xue Hu, Huanmei Chen},
TITLE = {Wind Speed Prediction Modeling Based on the Wavelet Neural Network},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {625--630},
URL = {http://www.techscience.com/iasc/v26n3/40022},
ISSN = {2326-005X},
ABSTRACT = {Wind speed prediction is an important part of the wind farm management and 
wind power grid connection. Having accurate prediction of short-term wind 
speed is the basis for predicting wind power. This paper proposes a short-term 
wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four 
wavelet neural network models using the Morlet function as the wavelet basis 
function were developed to forecast short-term wind speed in January, April, 
July, and October. Predicted wind speed was compared across the four models 
using the mean square error and regression. Prediction accuracy of model 4 
was high, satisfying the forecasting wind power industry requirements. 
Therefore, the proposed algorithm could be applied for practical short-term 
wind speed predictions.},
DOI = {10.32604/iasc.2020.013941}
}