Smart Meter Tampering Detection Using IoT Based Unsupervised Machine Learning

This work presents a novel smart grid tampering detection system re-engineered for end user monitoring and pipeline automation. The research focused on distributed energy resources. In context, the end user load profile, and generation capacity were processed in the cloud environment for tampering management. Computational pipelined methodology was adopted using baseline data from an independent electricity consumption data from 2018-2021 Abuja. First, a smart grid (SG) survey was carried using existing home estate at Abuja to ascertain tampering procedures in distributed energy resource domain. From the energy survey, the system architecture was developed and implemented based on computational model curve for dynamic attack vector mitigation.