Modeling and analysis of prediction of wind power generation in the large wind farm based on chaotic time series

The time series of wind power generating capacity are examined by nonlinear dynamical methods, in order to identify chaos characteristic from its random-like waveform. The analysis of modeling with low dimensions nonlinear dynamics indicates that time series of wind power generating capacity have chaos characteristic, and wind power generating capacity can be predicted in short time. Phase space reconstruction method is used for artificial neural network model design. The data from the wind farm located in the Saihanba China are used for this study.