Rival Penalized Competitive Learning-based Neural Network Model for Wind Power Forecasting

For increasing the accuracy of wind power forecasting, a rival penalized competitive learning-based radial basis function (RBF) neural network model was presented. Firstly the number of neural network hidden-layer-nodes and its initial center values were determined by rival penalized competitive learning. And then the width of RBF and the center values of network were identified accurately through K-means clustering. At last,appropriate weights of network were estimated by least mean square. The forecasting result shows that the presented model can lead to more accurate forecasting compared with the traditional neural network.