AI Storm Impact Forecasting

Applies AI to forecast storm-driven damage and customer impact using meteorology, vegetation, and network topology to pre-stage crews and materials.

The Problem

“Predict storm-driven outages and restoration needs”

Organizations face these key challenges:

High uncertainty translating forecast weather into feeder-level outages and damage, leading to misallocated crews, mutual aid, and materials

Slow, manual situational awareness (customer calls, field reports) delaying accurate ETAs and restoration prioritization

Regulatory, safety, and reputational exposure when restoration estimates are inaccurate or when critical infrastructure (hospitals, water plants) is not proactively supported

Impact When Solved

20–40% improvement in outage forecast accuracy at circuit/feeder granularity, enabling right-sized crew call-outs and staging5–12% reduction in major-event SAIDI through faster targeting of damage hotspots and optimized restoration sequencing$2M–$15M annual cost reduction from lower overtime/contractor spend and fewer unnecessary logistics deployments across multiple storm events

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