For comparison, the traditional BP method, the GABP model, and the RLWNN based adaptive forecast method were separately implemented.
We can conclude from Figure 6 that BP, GABP, and RLWNN have superior prediction ability when wind power output is located in region 1.
We can learn from Figure 7 that both GABP and RLWNN adaptive methods have a relative higher prediction accuracy compared with BP method under the circumstance when wind power output is unsmooth, especially oscillation.
Figure 8 shows the prediction results of GABP when the wind power output changes frequently, to illustrate the necessity of implementing an adaptive prediction method.
According to Figure 8, one can learn that starting at the 39th point, the operation condition of wind generator began to change, the relative prediction error of GABP was 2.5%, and when the operation point changed, the GABP method with fixed model parameters showed a decrease in prediction ability from the 39th to the 44th point.
According to the results in Figure 9, similar to the BP and GABP prediction methods, RLWNN adaptive method can be used to produce a relatively precise prediction result when the wind power output is smooth.
Using BP neural network, GABP network, and RLWNN method to predict the power load, the results and the error are illustrated in Figures 10 and 11, respectively.
We can learn from Figures 10 and 11 that, through a step-by-step improvement, the maximum prediction error can be reduced to 2% of the RLWNN adaptive method compared with 23.6% of BP method and 11.1% of GABP method.
The accuracy of GABP model is improved compared to the BP model, but the time complexity of GABP is relatively high.
Caption: Figure 8: Prediction results of GABP when operating point changes.