## 3 Results
### Algorithm Application Process  
In establishing the discrete conditional copula function, \(t\) and \(K\) must be determined. The confidence level \(\beta\) is a free parameter that can reflect the prediction accuracy, and it is unified as \(\beta\) = 0.9 in this paper. The values of parameter  
Figure 3: Pareto optimal solution set: P: point, P1: point 1, (**a**) region 1, (**b**) region 2, (**c**) region 3. It can be seen that the Pareto optimal solution sets of the three regions each have 12 best points, the PICP is greater than or equal to 0.7, and the PIAW are between 200 and 400 MW, which explains why the points in the solution set have better prediction performance. However, in Figure 3a,b, point 3 can be regarded as the sudden-change point of the entire fitting Pareto optimal solution set curve, which can also be said to be an inflection point, and in Figure 3c, the inflection point of the entire curve becomes point 4. In the figure, we mark the general Pareto optimal solution with a red dot and the inflection point with a blue circle. In the Pareto optimal solution set, the larger the PICP point, the larger the PIAW. The PIAW and PICP values are contradictory to each other; the solution sets cannot be compared with each other to seek the better one, so all solution sets are considered optimal solutions. In order to obtain the optimal parameter setting, we first use the extreme value method to standardize the PIAW and PICP values of each group of parameters. According to the definition in Section 3.2, the smaller the PIAW value, the better the prediction performance, so we use Equation (13) to standardize the index PIAW. Meanwhile, the larger the PICP value, the better the prediction performance, so we adopt Equation (14) to standardize the index PICP. \(x_{i}\) is the value to be normalized and \(x_{j}\) is the normalized value, \(MAX=\max(x_{i})(1\leq i\leq 12)\), \(MIN=\min(x_{i})(1\leq i\leq 12)\). \[x_{j}=\frac{MAX-x_{i}}{MAX-MIN} \tag{13}\]  
\[x_{j}=\frac{x_{i}-MIN}{MAX-MIN} \tag{14}\]  
Equations (13) and (14) are used to standardize the PIAW and PICP value of all points, and we consider that the two precision indexes are equally important for evaluating the effect of interval prediction. Therefore, the weights of PICP and PIAW can all be considered as 50%. Finally, we conduct a weight calculation for the two evaluation indexes of all the points in the Pareto optimal solutions set, with results shown in Tables 1-3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point** & **PICP** & **PIAW** & \begin{tabular}{c} **Standardized** \\ **PICP** \\ \end{tabular} & \
\begin{tabular}{c} **Standardized** \\ **PIAW** \\ \end{tabular} &
\begin{tabular}{c} **Weight** \\ **Calculation** \\ \end{tabular} \\ \hline P1 (\(t=1\), \(K=25\)) & 1.000 & 340.810 & 1 & 0.000 & 0.500 \\ P2 (\(t=1\), \(K=52\)) & 0.990 & 270.125 & 0.967 & 0.556 & 0.762 \\ P3 (\(t=1\), \(K=155\)) & 0.979 & 219.224 & 0.930 & 0.957 & 0.944 \\ P4 (\(t=1\), \(K=199\)) & 0.969 & 218.039 & 0.897 & 0.966 & 0.932 \\ P5 (\(t=1\), \(K=251\)) & 0.958 & 218.009 & 0.861 & 0.966 & 0.914 \\ P6 (\(t=1\), \(K=255\)) & 0.948 & 217.979 & 0.828 & 0.967 & 0.898 \\ P7 (\(t=1\), \(K=317\)) & 0.938 & 217.910 & 0.795 & 0.967 & 0.881 \\ P8 (\(t=1\), \(K=375\)) & 0.844 & 217.315 & 0.483 & 0.972 & 0.728 \\ P9 (\(t=1\), \(K=358\)) & 0.813 & 216.661 & 0.381 & 0.977 & 0.679 \\ P10 (\(t=1\), \(K=421\)) & 0.760 & 216.597 & 0.205 & 0.978 & 0.592 \\ P11 (\(t=1\), \(K=457\)) & 0.740 & 215.418 & 0.139 & 0.987 & 0.563 \\ P12 (\(t=1\), \(K=514\)) & 0.700 & 213.751 & 0 & 1.000 & 0.500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Weight calculation results of the points in the Pareto optimal solutions set in region 1.