AI Energy Theft Prevention

The Problem

“Detect and prevent electricity theft at scale”

Organizations face these key challenges:

High non-technical losses from meter tampering, bypassing, illegal connections, and billing manipulation, concentrated in specific feeders and customer segments

Inefficient field operations due to low hit-rates, manual triage, and limited investigator capacity, leading to high cost per confirmed case

Limited visibility into evolving theft tactics and coordinated fraud (e.g., intermittent bypassing, shared illegal taps), causing repeated losses and safety/reliability risks

Impact When Solved

30–50% inspection hit-rate using AI risk scoring vs. ~10–20% with random/rule-based selection$10–$20M annual revenue protection per $1B revenue utility by reducing NTL by 1–2 percentage points30–60% reduction in cost per confirmed theft and 50–80% faster investigation prioritization through automated, continuous anomaly detection

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0