The Nigerian Electricity Supply Industry (NESI) faces high Aggregate Technical, Commercial, and Collection (ATC&C) losses, averaging 35.22% in late 2024. This research proposes a framework leveraging smart meter data analytics to identify and mitigate these losses. By using high-frequency data from Advanced Metering Infrastructure (AMI), the study distinguishes between technical and non-technical losses (NTL). The methodology integrates machine learning (Random Forest, SVM) with load flow analysis. Findings provide a strategic roadmap for Distribution Companies (DisCos) to transition from reactive to proactive loss reduction, enhancing revenue and supply reliability.

Keywords

Smart Meters, Data Analytics, Loss Reduction In Nigeria, Machine Learning