## 3 Experiment and Results
### Experimental Results and Analysis

Table 5: Wind speed forecasting result for each site by different models in the second season. **Remark 2**. : _The evaluation index values obtained in Experiment II reveal that regardless of the optimization algorithm applied to optimize the combined model and the site used for forecasting, there is no optimal method for wind speed and wind power forecasting. For the second season of wind speed and wind power forecasting, the forecasting evaluation metrics of the combined models optimized by each algorithm had no significant differences._  
5.3 Experiment III: Verification of the Performance of the Combined Model Based on Five Sub-Models and Optimized by MODA  
In this experiment, 10 min intervals of wind speed and wind power forecasts in the third and fourth seasons were analyzed. The forecasting results for wind speed and wind power at four observation sites are shown in Figures 4 and 5, and the forecasting performance of each model, as evaluated by eight metrics, is shown in Tables 6 and 7. The results illustrate the following:  
Figure 3: The optimal combined model for wind power and wind speed forecasting. Figure 4: Wind speed and wind power forecasting result for each site in the third season. Figure 5: Wind speed and wind power forecasting result for each site by the CM5 model. (1) The combined model with five sub-models produced the smallest MAE, RMSE, STDAPE, U1, U2, and MAPE values and the largest DA and R\({}^{2}\) values at all sites. This indicates that the combined model has the best forecasting performance and stability. (2) For wind power forecasting, the results of the combined model with five sub-models (CM5) for four sites, as evaluated by eight metrics, were better than those of the other combined models. For example, the RMSE values of the CM5 model at the four sites were 40.1238, 32.9801, 56.0705, and 69.8088. Compared with the other combined models, the forecasting accuracy of the CM5 was greater. The main reason for this is that the CM5 can effectively use the advantages of each sub-model and the weight of the combined model optimized by the optimization algorithm. By comparing the CM5 with the CM2, CM3 and CM4 models, we determined that the number of sub-models included influences the forecasting performance of the combined model. The MAPE value of the CM5 was 12.00% lower than those of the other combined models for Site 1 in the third season. (3) For wind speed forecasting, the combined model obtained satisfactory forecasting results. The forecasting results of the combined models were better than those of the sub-models. Thus, the use of combined models can improve forecasting accuracy. For wind speed forecasting at Site 1, the MAPE values of the five sub-models were 14.61%, 16.31%, 18.46%, 21.04%, and 24.38%, respectively. In contrast, the MAPE value of the combined model (CM5) was 6.29%, an improvement of 8.32%, 10.02%, 12.17%, 14.75%, and 18.09%, respectively, compared with the five sub-models. (4) The models' goodness of fit results are shown in Table 6. The minimum value was 0.9665, which proves that the forecasting series obtained by combined model (CM5) was consistent with the actual series.