Wind speed forecasting method with multi-model optimization algorithm

Wind speed is with strong randomness and volatility,and the single algorithm model of wind forecasting is with low accuracy.To improve the forecasting precision of wind speed,a new wind speed forecasting method was proposed in this paper,it combined the gray model,the ant colony optimization algorithm and the genetic neural network.The improved grey model was given.Using the global optimization ability of ant colony algorithm,the weights of improved grey model was optimized with the least squares criterion,and the forecast of wind speed could be thus realized.In order to further improve the forecasting precision,genetic neural network was trained to further reduce errors,the results of gray model with ant colony optimization was taken as the inputs of the genetic neural network,and the measured wind speed data was taken as the output.The comparative analysis of forecasting results and field testing results in a certain wind farm showed that the forecasting method was with high accuracy and effectiveness.