## 4 Discussion  
In Figure 4 and Table 5, we obtained the subjective weights of each index calculated by analytic hierarchy process. Among them, the subjective weights of B1, B3, B6, and B7 are larger, while the subjective weights of B5, B8, and B10 are smaller. In Figure 5 and Table 6, we obtained the objective weights of each index calculated by the method of entropy weight. Among them, the objective weights of B3, B4, and B5 are larger, while the objective weights of B1, B2, B7, and B9 are smaller. There are some differences between subjective weight and objective weight, which is caused by their different characteristics. Subjective methods are more inclined to people's experience, while objective methods depend entirely on numerical values, so the results of the two methods will be different or even conflict.  
In Figure 7 and Table 7, we obtained the comparison of subjective weight, objective weight, and comprehensive weight. Among them, the comprehensive weights of B1, B3, and B6 are larger, while the comprehensive weights of B8 and B10 are smaller. We can see that the weight of the B7 index is larger when the subjective evaluation method determines the weight, while the weight of the B7 index is not so obvious in the comprehensive weight. The reason is that the B7 index is given a greater weight in the calculation of AHP is because of the human experience deviation. This paper corrects the human experience error by combining objective methods. The weight of the B5 index is very large when the objective evaluation method determines the weight, but the weight of the B5 index is not so obvious in the comprehensive weight. The reason is that the B5 index has a large error when it is calculated by using the method of entropy weight because of the insufficient amount of data. This paper combines the subjective method to correct the data error. From the Figure 7, it can be seen that the comprehensive weight of each index is between that determined by the subjective method and objective method. The comprehensive weights obtained by the minimum information discriminant principle can overcome the shortcomings of the analytic hierarchy process (AHP) and the entropy weight method. The comprehensive weight has the advantages of both subjective weight and objective weight. It can make full use of the existing data and reduce the large errors caused by inadequate or inaccurate raw data by combining subjective experience.  
In Figure 8, we obtained the correlation degree between the four schemes and the ideal scheme under each index calculated by grey correlation analysis method. The correlation degree of each  
Figure 8: Relevance coefficients of various schemes under various indicators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & **Scheme 1** & **Scheme 2** & **Scheme 3** & **Scheme 4** \\ \hline Correlation degree \(\gamma\) & 0.6627 & 0.5986 & 0.6424 & 0.7310 \\ Ranking & 4 & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ranking of schemes.  
index of scheme 4 is higher. The correlation degree of some indicators in scheme 1 and scheme 3 is higher, and that of individual indicators is lower. The correlation degree of most of the indicators in scheme 2 is lower. In Table 8, we can see the correlation degree and ranking of four schemes with the ideal scheme. Scheme 4 is the best scheme, and scheme 2 is the worst one. In practice, scheme 4 has high reliability, high power quality, and good environmental protection. Scheme 2 has the lowest reliability and poor power quality although it has low cost. Scheme 1 is moderate in all aspects and has no obvious advantages and disadvantages. Scheme 3 has high reliability, but its cost is very high. The final evaluation results are consistent with the actual situation. This result reflects the correctness and rationality of the comprehensive evaluation method in this paper. Generally speaking, the evaluation method used in this paper conforms to the actual engineering effect and has certain social application value.