## 3 Results and Discussion
### Performance of Combined Model
The red line representing the basic model's predicted values essentially envelops the blue line of the combined model's predicted values, indicating that the combined model achieves closer approximations to the actual values at the majority of data points. Figure 7b illustrates the absolute errors between predicted and actual power values for both the basic and combined models. It is evident that the combined model exhibits lower absolute errors than the basic model in most instances. Statistical analysis of these absolute errors reveals that the basic model has a mean absolute error of 0.38 MW and a median of 0.27 MW, whereas the combined model achieves a mean absolute error of 0.26 MW and a median of 0.17 MW. These findings demonstrate that the combined model exhibits a 32.70% improvement in mean absolute error and a 35.49% improvement in median absolute error compared to the basic model. Figure 8 illustrates the absolute percentage error distribution of the basic and combined models on the test set, and Table 2 presents the statistical distribution of the absolute percentage error. Figure 7: Comparison of predicted performance between the basic model and the combined model. (**a**) Power predicted by both models along with the actual power values, and comparison of absolute percentage errors; (**b**) comparison of absolute error. Figure 8 and Table 2 reveal that the combined model outperforms the basic model in overall accuracy distribution. Specifically, the proportion of data points with errors ranging from 0-0.25% increased from 50.32% in the basic model to 68.08% in the combined model. Additionally, there was a reduction in the number of high-error data points, with those exceeding 1% error decreasing from 156 to 73, and their percentage dropping from 4.921% to 2.303%. The test set comprises a total of 3170 data points. The number of data points with prediction errors less than 0.25% increased from 1595 in the basic model to 2158 in the combined model. Conversely, the number of data points with errors exceeding 0.25% decreased from 1575 in the basic model to 1012 in the combined model. While observing the data points within the 0.25-0.5% error range in Table 2, it can be seen that the absolute count decreased from 856 in the basic model to 691 in the combined model, which seems modest at first glance. However, considering the relative proportion of these data points within the overall error range exceeding 0.25%, it is found that the basic model accounted for 54.35%, while the combined model improved to 68.28%. This indicates that the combined model not only reduced the number of high-error points but also shifted more of the data points originally falling within the 0.25-0.5% error range to a lower error interval. It can be observed that the combined modeling approach improves the error distribution, increasing the proportion of data points with small prediction errors, thus enhancing the model's stability and enabling it to provide more reliable power predictions for real-world applications. This indicates the effectiveness of the combined model in enhancing prediction accuracy. Next, we employ cross-validation techniques to assess the stability and predictive capability of the combined model. The original dataset consisting of 15,848 data points is partitioned into five mutually exclusive subsets. For each round, the union of four subsets serves as the training set, while the remaining subset acts as the test set, generating five sets of training and test data for five rounds of training and testing. The results are shown in Table 3.