## 5 Experiment and Result Analysis
### Result Analysis  
To further verify the prediction effect of the proposed model, a total of six comparison models were set up in this study: Temporal Convolution Networks (TCN), Gate Recurrent Unit (GRU), SVM, WLSSVM, SAO-WLSSVM, and MISAO-WLSSVM. The prediction model was visualized and analyzed for three days selected for three weather types, namely sunny, cloudy, and rainy, while keeping the parameters constant, as shown in Figures 8-10. Figure 7: Sunny power decomposition results. Figure 8 shows the predicted results under sunny conditions. The power variation during sunny weather was moderate, and the overall prediction effect was the best. The prediction results of the improved prediction model were closer to the actual values, and Figure 9 shows the predicted results under cloudy conditions. Owing to continuous changes in cloud cover, solar irradiance also changes with both gentle and abrupt changes. In the  
Figure 8: Comparison of forecast results of sunny weather. Figure 10: Comparison of forecast results of rainy weather. Figure 9: Comparison of forecast results of cloudy weather. abrupt changes, the model in this study performed well, proving its strong anti-interference ability. Figure 10 shows the prediction results under rainy day conditions. The power amplitude is lower during rainy weather, and the power change is more evident owing to the long fluctuation period and significant volatility. The model prediction effect is reduced; however, this model still has some advantages in terms of prediction accuracy and stability. Using three evaluation metrics (MAPE, RMSE, and MAE) on the prediction effect of the TCN, GRU, SVM, WLSSVM, SAO-WLSSVM, MISAO-WLSSVM, and MISAO-ICEEMDANWLSSVM prediction models for the three weather types, the calculation of the error yields the evaluation results as shown in Table 7.  
Table 7 shows that, in comparison to the individual TCN, GRU, SVM, and WLSSVM models, all of the combined prediction models outperform the single model in a variety of weather scenarios. This suggests that the WLSSVM network's hyperparameters specifically need to be optimized. By comparing the prediction results of the SAO-WLSSVM and MISAO-WLSSVM models, it was found that all of the indicators of the MISAO-WLSSVM model were lower than those of the SAO-WLSSVM model, and the average absolute error percentage MAPE under different weather conditions decreased by 22.9%, 36.4%, and 20.9%, respectively. The average absolute error MAE percentage under different weather conditions decreased by 15.0% and 20.9%, respectively. MAE decreased by 15.7%, 24.8%, and 15.0%, respectively, and the root mean square error MASE decreased by 12.0%, 25.1%, and 13.9%, respectively, under different weather conditions, indicating that MISAO has a better ability to find the optimum and stability than SAO. In addition, the MISAO-ICEEMDAN-WLSSVM hybrid model obtained the best value among all of the evaluation indicators, in which the indicator MAPE under different weather conditions decreased by 25.3%, 29.1%, and 32.8% compared to that of MISAO-WLSSVM, respectively; the indicator MAE under different weather conditions decreased by 39.0%, 17.8%, and 19.9%, respectively; under different weather conditions, the indicator RMSE decreased by 37.2%, 16.0%, and 13.3%, respectively. The prediction results show that the use of ICEEMDAN decomposition can effectively eliminate redundant noise, extract the main features of the historical PV data, and significantly improve the prediction accu