Short Term Wind Speed Forecast Using Optimized Neural Networks

The paper investigate a real-world application, the short-term wind speed forecasting in a wind farm using parametric recurrent neural networks as forecast model. To deal with the complexity of the process and to improve the performance of the model, a special on-line learning algorithm is employed for training the parametric recurrent networks, based on the windowing method. Using an improved activation function (AF) and a parameter, p, one is able to get the optimize size of the needed network. This is possible by training the shape of AF. After finishing the training by checking the value of the p, it is possible to reduce and optimize the size of the network to find out the minimum size of the network. Finally, simulation results for the numerical method and parametric neural networks are shown.

Keywords:

Short-term , wind speed , forcast , optimum size , neural netwroks