Predicting the Unpredictable: Radio Frequency Mesh Degradation Forecasting Model for Advanced Metering Infrastructure (AMI) Network
List of Authors
Eric Gideon, Faizir Ramlie, Mohd. Yazid Abu, Nolia Harudin, Siti Uzairiah Mohd. Tobi, Wan Zuki Azman Wan Muhamad, Zamzuraida Baharum
Keyword
Advanced Metering Infrastructure (AMI), Radio Frequency Mesh (RF Mesh) Degradation, Forecasting Model
Abstract
Advanced Metering Infrastructure (AMI), a megaproject has its own challenges especially maintaining post deployment targets. One of them is to adhere to the Service Level Targets. Radio Frequency Mesh (RF Mesh) is a prominent AMI Wireless NAN technology. However, wireless networks are prone to degradation in performance, which is associated with data loss. Ensuring a high-quality RF link produces accurate meter readings, resulting in precise billing. Forecasting RF Mesh Link Degradation will be able to assist electrical utility service providers in the management and maintenance of their AMI RF Network. A systematic literature review (SLR) was conducted utilising secondary data from 37 publications and concluded that combination of Machine Learning and Time Series technique is the best model that can be used to develop RF Mesh Link Degradation, emphasising on RSSI as the Key Performance Indicator (KPI).