## 6 Conclusions  
This paper presents a method for technical criteria selection using ML for electrical distribution substation communication technology solution. Manually selecting an appropriate communication technology as a communication solution for an electrical distribution substation can be difficult, especially when numerous factors and criteria are involved. Thus, in this paper, ML was used to aid in the selection process by short-listing the numerous technical criteria of the potential communication technologies, based on the influences of each criterion on the communication technology selection. More specifically, this paper provided a list of the potential communication technologies to be applied at the electrical distribution substation, based on an extensive literature review. From the list provided, the technical criteria (or specifications) of each of the communication technologies were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Criteria** & **Average** \\ \hline Frequency & 0.0127919 \\ \hline Distance & 0.0086610 \\ \hline Scalability & 0.0048568 \\ \hline Data rate & 0.0037547 \\ \hline Reliability & 0.0032249 \\ \hline Physical Topology & 0.0013349 \\ \hline Frequency Bandwidth & 0.0003337 \\ \hline Terrain Factor & \(-\)0.0016518 \\ \hline Interference & \(-\)0.0067725 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ranked technical criteria based on the average values of the output of the ‘Explain Prediction’ operator.  
identified for the creation of the ML training dataset. This dataset was used for a supervised ML process in the RapidMiner software package, where a thorough investigation into the performance of several ML models in the classification of the communication technology criteria was conducted. From the investigation, the naive Bayes model showed the highest overall performance in terms of accuracy and execution time. The output from the naive Bayes' analysis was then used to rank and eliminate the technical criteria that held negative attributes for the selection of communication technology for electrical distribution substation. The ranking of these criteria was expected to simplify the selection process of the best communication technology for the electrical distribution substation. As for future work, the remaining seven technical criteria will be evaluated further to determine the most suitable communication technology for the electrical distribution substation.  
N.A.A. (Writing--original draft preparation, algorithm, methodology and result); N.A.M.R. (Investigation, project administration and supervision); K.H.M.A. (Investigation and Writing--review and editing); F.S.S. (Investigation and resources); A.M.Z. (Funding acquisition and validation). All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the UNITEN R & D Sdn Bhd through TNB Seed Fund grant (UTD-RD-20-03).  
Not applicable.  
We would like to thank UNITEN R & D Sdn Bhd for funding the project under TNB Seed Fund grant (UTD-RD-20-03). We would also like to express our gratitude for the continuous support given by TNB-DN, TNB-AM, TNB-ICT, TNB-AMI, TNB-ES and team members of the project under TNB Seed Fund grant (UTD-RD-20-03).  
The authors declare no conflict of interest. The funders (TNB) have validated and agreed with the listed communication technologies for the electrical distribution substation. The ML algorithm framework and results presented in this study are not directly influenced by TNB's opinions.