## 5 Discussion and Conclusions  
A prediction model based on a fully connected neural network is proposed, which can predict the AWTT accurately. In order to remove the coupling among the original inputs and improve the efficiency of the network model, the factor analysis algorithm is used. Furthermore, without reducing the prediction accuracy of the AWTT, less data are used to reduce the dimension of data processing. The conclusions are as follows:  
1. After factor analysis is adopted, the coupling among ambient temperature, load current, and top oil temperature can be effectively removed. Two key parameters, the temperature factor and load factor, are proposed to reduce the complexity of the network model, which reduces the amount of data input and improves the efficiency;
2. The prediction model based on factor analysis and a fully connected neural network can accurately obtain the AWTT. The RMSE between the predicted results and the measured values of the AWTT is 0.62498, and the \(R^{2}\) is 0.9714, which shows the feasibility of the proposed method; \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Textual Method** & \
\begin{tabular}{c} **Hot Circuit** \\ **Guide Method** \\ \end{tabular} &
\begin{tabular}{c} **Method without** \\ **Factor Analysis** \\ \end{tabular} & **L-M Method** \\ \hline RMSE & 0.6250 & 14.7216 & 1.9175 & 0.8253 \\ MAE & 0.6027 & 3.1368 & 0.9761 & 0.6819 \\ MAPE & 2.31\% & 12.40\% & 3.79\% & 2.58\% \\ \(R^{2}\) & 0.9714 & 0.1835 & 0.8474 & 0.9563 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy evaluation table corresponding to different methods. 3. Compared with the thermal circuit guideline method, the L-M method and the method without factor analysis, the results show that the prediction method proposed in this paper has higher accuracy and reliability. Combined with the predicted results, the abnormal values can be removed and corrected to make the AWTT prediction more accurate, which is of great significance for the prediction of the winding hot-spot temperature and transformer state assessment. On the basis of collecting historical state data of transformers, the proposed method can be applied to establish a unique and accurate AWTT prediction model for each transformer. It helps to fully analyze and mine monitoring data, promoting the development of the power supply system from maintenance after faults and planned maintenance to maintenance based on transformer state. This can detect safety hazards in advance, improve operation and maintenance efficiency, and reduce costs. In addition, further research will be conducted on transformers of different voltage levels and models, to improve the applicability of the method proposed in this paper. Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON] and Z.F. ; validation, [PERSON], [PERSON] and W.J. ; formal analysis, [PERSON]; investigation, [PERSON] and H.Z. ; resources, [PERSON]; data curation, [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and W.J. ; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; All authors have read and agreed to the published version of the manuscript. This research was funded by the State Grid Shanxi Electric Power Company Technology Project, grant number 5205K0230006. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Authors [PERSON] and [PERSON] were employed by the company State Grid Jinzhong Electric Power Supply Company. And author [PERSON] was employed by the company State Grid UHV Transformation Co. of SXPC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.