## 1 Introduction  
The traditional power distribution network is designed to distribute electricity and information in a unidirectional flow from the power transmission network to consumers through electrical distribution substations. The introduction of distributed energy resources (DERs) and many other end-user applications, such as electric vehicles (EVs), advanced metering infrastructure (AMI), and smart appliances, to the distribution grid has resulted in a shift in the supply and demand trend of electricity. However, the traditional distribution communication networks are not suitably designed for these additional connections [1]. This necessitates two-way communication in the distribution substation in order to accommodate the fast response required for supply and demand from consumers for effective power distribution. Furthermore, the expected exponential growth in the population, the anticipated increase in DERs, and the rise of end-user applications connected to the distribution networks would place further strain on the current communication infrastructure in the distribution grid. This is due to the expected increment in power, bandwidth, and data rate demands. As a result, the distribution grid will face a multitude of difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure. Therefore, it is necessary to investigate viable future communications technology to cater for these future demands, particularly for the communication within and between electrical distribution substations. However, the manual selection process for the appropriate communication technology for the distribution substation is frequently difficult and complex due to the presence of numerous factors and specification criteria that must be considered, such as bandwidth, frequency, data rate, distance coverage, topologies, and geographical limitations. Other considerations, aside from the technical criteria, include costs, standards, technology maturity, and so on, all of which will influence the selection of the appropriate communication technology. Thus, in this study, machine learning (ML) was used to aid the decision makers in the selection of the best communication technology for distribution substations based on the analysis of the influences of the criteria on the selection process. In short, the objectives of this paper were to find the optimal performance for the ML models in criteria selection, rank the criteria from the most important to the least important, and evaluate the criteria that most strongly influence selecting the best communication technology for the electrical distribution substation. Other considerations, aside from the technical criteria, include costs, standards, technology maturity, and so on, all of which will influence the communication technology selection. Manually selecting suitable communication technologies can be complex, especially when numerous criteria are involved. Thus, in this work, machine learning (ML) was used to aid in the selection of the best communication technology for electrical distribution substation based on the analysis of the influences of the criteria on the selection process. In short, the objectives of this paper were to find the optimal performance for the ML models in criteria selection and to omit some technical criteria that least influence the selection of the best communication technology for the electrical distribution substation. The first step for criteria selection using ML for electrical distribution substation communication technology was to identify the potential communication technologies for the substations. Then, based on several important criteria in the literature, a dataset for communication technology specification was created. The dataset was fed into one of the ML software packages, RapidMiner, in which several ML models were selected to find the model with the highest performance in terms of prediction accuracy.