A hybrid strategy of short term wind power prediction

Two different prediction methods are investigated for short term wind power prediction of a wind farm in this paper. The adopted strategies are individual artificial neural network (ANN) and hybrid strategy based on the physical and the statistical methods. The performance of two prediction methods is comprehensively compared. The calculated results show that the individual ANN prediction method can yield the prediction results quickly. The prediction accuracy is low and the root mean squared error (RMSE) is 10.67%. By contrast the hybrid prediction method operates costly and slowly. However, the prediction accuracy is high and the RMSE is 2.01%, less than 1/5 of that by individual ANN method. Meanwhile, it is found that the errors of the prediction have some relation with the wind speeds. The prediction errors are small when the wind speeds lower than 5 m/s or higher than 15 m/s. The reasons for such phenomena are also investigated.

► The physical strategy and ANN technique are effectively integrated.
► The wind sources at each turbine, the variation of generator power curve to time and environment are considered.
► The final results of the whole wind farm are significantly reduced due to the averaging effect.
► Under the ANN based prediction, with the MAE values more than 4000 kW and the NRMSE values about 11%.
► Under the hybrid method, with the MAE less than 760 kW and the NRMSE around 2%.