Smart Meter Data Analytics for Power Theft and Loss Hotspot Detection

Objective: To develop a system that identifies and visualizes power theft and loss hotspots using smart/distribution meter data for DISCOM engineers.

Unaccounted power losses due to theft and technical inefficiencies significantly impact the revenue and operational efficiency of Indian power distribution companies (DISCOMs), affecting reliable electricity supply to consumers. This challenge is particularly acute in urban and rural areas with high aggregate technical and commercial (AT&C) losses, as tracked by state electricity departments and linked to SDG 7 (Affordable and Clean Energy).

The proposed solution is a digital platform that ingests smart/distribution meter data, applies anomaly detection and spatial clustering to flag potential theft or loss hotspots, and provides actionable visualizations for local DISCOM engineers. The solution leverages open datasets, machine learning, and geospatial analytics for scalable deployment in diverse Indian contexts.

Key features include secure ingestion of meter data (from open mock datasets), unsupervised anomaly detection algorithms, integration of local grid and census maps (using OpenStreetMap and Bhuvan), hotspot heatmaps, and a reporting dashboard. The working prototype will demonstrate detection accuracy and actionable insights on a real or mock dataset.

Measurable impact includes reduced AT&C losses, improved grid reliability, and data-driven enforcement. The solution can be scaled to multiple districts and states, empowering DISCOMs and supporting government transparency and SDG 7 targets.