Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations

In the future, as populations grow and more end-user applications become available, current traditional electrical distribution substation will not be able to fully accommodate new that may arise. Consequently, there numerous difficulties, including network congestion, latency, jitter, and, in worst-case scenario, failure, among other things. Thus, purpose of this study is assist decision makers selecting most appropriate communication technologies for an through examination criteria’s in-fluence on selection process. study, nine technical criteria were selected processed using machine learning (ML) software, RapidMiner, find optimal criteria. Several ML techniques studied, Naïve Bayes was chosen, it showed highest performance rest. From ranked order importance from important least based average value obtained output. Seven identified being should evaluated determine technology solution a result study.