Short-term wind speed forecast model for wind farms based on genetic BP neural network

To improve the short-term wind speed forecasting accuracy for wind farm, a prediction model based on back propagation(BP) neural network combining genetic algorithm was proposed. Autocorrelation analysis was used to discover historical wind speeds which have significant influence on predicted wind speed. The input variables of BP neural network predictive model were historical wind speeds, temperature, humidity and air pressure. Genetic algorithm was used to optimize the weights and bias of BP neural network. Optimized BP neural network was applied to predict wind speed an hour before, two hours before and three hours before individually. The simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.