Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.
