Abstract

Daily and real-time forecast data of wind power is predicted in this study using three methods, which are Kalman filter model, GARCH model and time-series-based BP neural network model. Then, owing to evaluation to the calculation of accuracy and qualification rate, the best method, the time-series-based BP neural network model, was selected for its highest accuracy. Moreover, the prediction error influence due to convergence of wind turbine is on consideration according to the evaluation. Finally, suggestions of improving the prediction accuracy were put forward based on the discussion of accuracy-obstacle factors.

Keywords:

BP neutral network, Kalman filter, time sequence prediction, 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.