Chaotic characteristics analysis and prediction for short-term wind speed time series

Abstract

A short-term wind speed time series prediction is studied. First, 0-1 test method for chaos is used to identify the short-term wind speed time series that has chaotic characteristics. Through phase space reconstruction, the delay time is determined by using C-C algorithm; and the embedding dimension is determined by using G-P algorithm. Then a least square support vector machine with parameters online modified is proposed, so that an improved particle swarm optimization algorithm may be used for the prediction of parameters optimization. Simulation experiment shows that the present method for its prediction accuracy, prediction error, and prediction effect is better than other prediction methods. Thus the proposed prediction method is effective, and feasible.