Abstract

Firstly, this study used prediction methods, including Kalman filter method, the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model and the BP neural network model based on time sequence, to predict real-timely the wind power. And then, we construct indexes such as mean absolute error, root-mean-square error, accuracy rate and percent of pass to have error analysis on the predictive effect and get the best results of prediction effect that based on time sequence of the BP neural network model. Finally, we concluded the universal rule between the relative prediction error of single typhoon electric unit power of and the prediction relative error of total machine power by the analysis into lateral error indicators. And we analyze the influence on the error of the prediction result that resulting from the converge of wind generator power.

Keywords:

BP neural network model, GARCH model, kalman filter method, wind power prediction,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.