## 5 Simulation Experiments and Result Analysis
### Simulation Experiments on Intelligent Power Distribution and Utilization Dataset (II)
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{4}{c}{**Sample No.**} & \multicolumn{4}{c}{**Sample No.**} \\ \cline{2-9}  & **1** & **2** & **3** & **4** & **Index** & **1** & **2** & **3** & **4** \\ \hline User number of & & & & & & & & \\ public users & 0.0000 & 0.0002 & 0.2496 & 0.5849 & \begin{tabular}{c} Transformer number of \\ public users \\ \end{tabular} & 0.0000 & 0.0002 & 0.2499 & 0.5876 \\ \hline Transformer capacity of & & & & & & & \\ public users & 0.0255 & 0.0182 & 0.3376 & 0.5819 & \
\begin{tabular}{c} User number of \\ specialized users \\ \end{tabular} & 0.0390 & 0.1093 & 0.2618 & 0.7129 \\ \hline Transformer number of & & & & & & & \\ specialized users & & & & & & & \\ \hline Total number of & & & & & & & \\ transformers & 0.0144 & 0.0305 & 0.2686 & 0.6518 &
\begin{tabular}{c} Transformer capacity of \\ personalized users \\ \end{tabular} & 0.3074 & 0.4216 & 0.5614 & 0.8277 \\ \hline Total number of & & & & & & & \\ transformers & 0.1946 & 0.2515 & 0.6253 & 1.0000 \\ \hline Total number of & & & & & & \\ electricity users & & & & & & \\ \hline Length of power cable line & 0.4209 & 0.3221 & 0.7628 & 0.3740 & Total length of power line & 0.0287 & 0.0501 & 0.7030 & 0.9300 \\ \hline Number of switching & & & & & & & \\ equipment & 0.0188 & 0.0258 & 0.0500 & 0.5296 & Average segment number & 0.1187 & 0.1115 & 0.4622 & 0.3431 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized data of typical samples. from the real sample space. However, the new proposed GANs-HDI algorithm holds good resistance to the changes of information loss rates, and showed its outstanding stability. When considering the influence of sample number on the algorithm performances shown in Section 4, it would be necessary to study on the relationship between data integration performance and parameters in GANs-HDI. In order to further proof the influences of incomplete sample proportion and information loss rate on the heterogeneous data integration performance of GANs-HDI, deviation rates were calculated with different incomplete sample proportions and information loss rates on IPDU heterogeneous datasets, as shown in Figure 5.  
In Figure 5, the color of each color block indicates the reciprocal of the mean of deviation rates from 10 independent repeated experiments with same incomplete sample proportion and information loss rate. The brighter the color, the better the algorithm works. With the decreases of incomplete sample proportion and information loss rate, data integration performance of GANs-HDI algorithm gradually improves. In Figure 4, the boundaries of deviation rate 20% and 50% were marked. It is obvious that, when the incomplete sample proportion is less than 30%, and the information loss rate is less than 20%, the confidence of IPDU heterogeneous data integration is considerably higher. Generally speaking, the larger volume of dataset is, the higher accuracy of data integration would be, and the  
Figure 5: Deviation rate of generative adversarial networks based heterogeneous data integration (GANs-HDI) algorithm on heterogeneous datasets for intelligent power distribution and utilization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Information Loss Rate/\%** & **Deviation Rate/\%** & **RMSE** \\ \hline \multirow{3}{*}{k-NN} & 5 & 29.26 & / \\  & 15 & 38.72 & / \\  & 30 & 102.16 & / \\ \hline \multirow{3}{*}{BP} & 5 & 45.56 & 0.7980 \\  & 15 & 63.22 & 0.8384 \\  & 30 & 88.36 & 1.0916 \\ \hline \multirow{3}{*}{GANs-HDI} & 5 & 14.59 & 0.1460 \\  & 15 & 19.73 & 0.1975 \\ \cline{1-1}  & 30 & 26.01 & 0.2603 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison on heterogeneous datasets for intelligent power distribution and utilization. confidence level of the results of heterogeneous data integration of distribution network will also show an overall upward trend.