## 3 Results and Discussion
### Performance of the Combined Model to Changes in Training Data Size  
In this section, the impact of training size on the performance of the combined model is conducted. The training set is sorted in chronological order, and the first 10% (i.e., the first 1268 points), 20% (2526 points), 30% (3803 points), 40% (5071 points), 50% (6339 points), 60% (7607 points), 70% (8875 points), 80% (10,142 points), 90% (11,410 points), and 100%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Combined Model** & **RF [32]** & **GBDT [33]** \\ \hline MAPE & 0.2346\% & 0.2733\% & 0.3213\% \\ MaxAPE & 2.393\% & 8.847\% & 3.693\% \\ R\({}^{2}\) & 0.9982 & 0.9970 & 0.9970 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy Comparison of Different Algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Round Label**} & \multicolumn{2}{c}{**MAPE**} & \multicolumn{2}{c}{**MaxAPE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-7}  & **Basic Model** & **Combined Model** & **Basic Model** & **Combined Model** & **Basic Model** & **Combined Model** \\ \hline
4 & 0.3541\% & 0.2298\% & 3.216\% & 2.323\% & 0.9967 & 0.9984 \\
5 & 0.3626\% & 0.2421\% & 3.559\% & 3.144\% & 0.9963 & 0.9981 \\ Average & 0.3521\% & 0.2336\% & 2.868\% & 2.839\% & 0.9966 & 0.9983 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._(12,678 points) are selected as training sets to establish power prediction models. The clustering number of these combined model is 10. The error distribution of the prediction models is illustrated in Figure 9, and the corresponding model prediction errors are presented in Table 5.  
As observed from Table 5 and Figure 9, with the increase in the number of samples, the median prediction error and MAPE of the model gradually decrease, indicating an overall improvement in prediction accuracy. Simultaneously, as the number of training set samples increases, the maximum error also exhibits a downward trend. The slight fluctuations observed within the 10-30% range may be attributed to the inherent randomness of the maximum values. The top 75% errors also show a decreasing trend, demonstrating that the model's prediction errors are gradually being controlled as the number of sampling points increases. However, there are also fluctuations within the 10-30% range, which could be because this range mostly covers operating conditions within the same season, resulting in less significant improvements in prediction accuracy for other seasons. Inaccuracies in predictions for other seasons often lead to the occurrence of high error points. When the sampling quantity reaches 70%, the model's prediction accuracy is significantly enhanced, with an average error of 0.6677% and a maximum error controlled within 5%, specifically 4.035%. Additionally, 75% of the prediction points have errors less than 0.6306%. This may be because, at 70% samples, the training set basically covers typical operating conditions for three seasons. Therefore, to ensure controllable prediction errors in the model, it is advisable to collect data that comprises at least 70% of the annual data volume as the training set.