AI Smart Meter Analytics

The Problem

“Turn smart meter data into actionable insights”

Organizations face these key challenges:

High-volume interval data (15/30/60-minute) is noisy, incomplete, and difficult to operationalize across AMI, OMS, CIS, and GIS systems

Revenue protection teams are overwhelmed by false positives from rule-based alerts, leading to low investigation efficiency and delayed recovery

Limited visibility into low-voltage network conditions and behind-the-meter behavior increases outage duration, power-quality complaints, and peak procurement costs

Impact When Solved

0.2–1.0% reduction in non-technical losses and 20–50% higher theft/tamper investigation hit rate30–60% faster detection/localization of outages and voltage issues with 10–25% fewer unnecessary truck rolls1–3% better short-term load forecast accuracy and 5–15% improved demand response targeting and verification

The Shift

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