Short-term forecasting optimization algorithm for unsteady wind speed signal based on wavelet analysis method and neutral networks method

To promote the forecasting performance of traditional neural networks for non-stationary wind speed signal, an optimization algorithm was proposed based on wavelet analysis method and neural networks method. This optimization algorithm employed wavelet analysis method to make signal decomposition and reconstruction calculations for original wind speed series attain more steady sub-series. Then BP neural networks method was used to build unsteady prediction models for each layer to realize multi-step rolling forecast calculation. Simulation results show that the optimization algorithm can attain high-precision multi-step ahead forecast results, respectively improve forecast precision of traditional BP neural networks method by 55.56%, 32.43% and 34.58%, and the mean relative error of one-step, three-step and five-step ahead forecast are 0.48%, 1.50% and 2.97%. The optimization has signal decomposition and self-learning ability.