## 5 Simulation Experiments and Result Analysis
### Simulation Experiments on Intelligent Power Distribution and Utilization Dataset (I)  
In this section, the simulation experiment took power cable test data of sixty 22 kV XLEP power cable samples for performance comparison of heterogeneous data integration in the JetBrains PyCharm 2017.2 environment. The power cable tests include the accelerated thermal aging tensile fracture test, accelerated thermal extension test, differential scanning calorimetry test, breakdown test, and DC leakage current test. In this simulation experiment, the incomplete sample proportion in the total samples was set to be 20%, and information loss rates were set to be 15%. Incomplete data sample and missing indexes were randomly selected. According to the incomplete sample proportion, 12 samples were randomly selected from total 60 data samples as the incomplete samples. Then, in these chosen samples, two indexes were randomly picked out to delete their corresponding information, and formed a set of data samples to be repaired, according to the information loss rate. Insulating state test indicators of 22 kV XLEP power cable are as shown in Table 5. After the data restoration, this section employed support vector machine (SVM) to predict targeted power cable samples' relative aging times, where 15 samples were treated as the test group, and the other 45 samples were the training ones. In order to verify the data integration performance of the proposed GANs-HDI algorithm, k-nearest neighbors (k-NN), and error-back propagation (BP) were chosen as control groups with parts of the model parameters selected by experience. The cluster number was equal to the sample class number in k-NN algorithm. The number of hidden neural nodes was set to be 10 in BP algorithm with Sigmodal function as the activation function. In the proposed GANs-HDI algorithm, the threshold of discrimination rate was set to be \(c=0.05\), the reducing pace was set to be \(\alpha=0.0005\), the number of hidden neural nodes was set to be \(L=10\), initialized threshold values as \(R_{1}=0.85\) and \(R_{2}=0.6\), clustering threshold values as \(k=2\), Sigmodal function was chosen as the activation function, and new generation proportion \(\overline{N}/N\) was set to be 0.5. Repair the incomplete data samples with k-NN, BP, and GANs-HDI algorithms, respectively, and calculated the deviation rate with the real values. After the data restoration, employ SVM to perform the relative aging time prediction tasks. Repeat 10 trials independently, and calculate the averages and RMSE of the accuracy values of data restoration results, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Restoration Deviation Rate/\%** & **Life Prediction Deviation Rate/\%** & **RMSE** \\ \hline SVM & / & 24.61 & / \\ k-NN + SVM & 40.26 & 58.65 & / \\ BP + SVM & 28.58 & 65.61 & 0.6216 \\ GANs-HDI + SVM & 22.70 & 86.62 & 0.2646 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison on heterogeneous datasets for intelligent power distribution and utilization. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{3}{c}{**Sample No.**} & \multicolumn{3}{c}{**Sample No.**} \\ \cline{2-7}  & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline Real operation time/year & 4 & 7 & 11 & Breakdown test pressure level diff. 1 & 17 & 17 & 17 \\ Relative aging time & 0.15 & 0.27 & 0.52 & Breakdown test pressure level diff. 2 & 14 & 11 & 7 \\ Elongation at break (\%) & 240 & 225 & 190 & Breakdown test & 0.13 & 0.21 & 0.38 \\ Load elongation (\%) & 23.7 & 47.6 & 97.1 & Insulation resistance per unit length/G/G & 41 & 29 & 19 \\ Permanent elongation (\%) & 2.1 & 3.50 & 6.4 & Operating ambient temperature/\({}^{\circ}\)C & 90 & 90 & 90 \\ DSC peak temperature/ & 263 & 258 & 243 & Operating ambient temperature/\({}^{\circ}\)C & 90 & 90 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Insulating state test indicators of 22 kV XLEP power cable [14].