Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

In this paper, a Higher Order Wavelet Neural Network (HOWNN) trained with an Extended Kalman Filter (EKF) is implemented to solve the wind forecasting problem. The Neural Network based scheme is composed of high order terms in the input layer, two hidden layers, one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid, and an output layer with a linear activation function. A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network. The size of the regression vector is determined by means of the Lipschitz quotients method. The proposed structure captures more efficiently the complex nature of the wind speed time series. The proposed model is trained and tested using real wind speed data values.