Accelerating Storm Restoration with AI-Powered Outage Intelligence

Challenge – Long Restoration Times and Inaccurate ETRs

A major utility provider serving 10 regions faced significant challenges during large storm events — with prolonged restoration times, inaccurate estimated time of restoration (ETR), and inefficient crew dispatching.
Manual coordination and limited visibility into storm impact data led to slower response times, inconsistent communication, and increased operational costs during major outage incidents.

Solution – AI-Powered Outage Intelligence with Maestro and AWS

Partnering with Nuodata, the utility implemented an intelligent Outage Intelligence framework powered by Maestro (Nuodata) and the Outage Intelligence Pack to transform restoration operations.

The solution included:

• Streaming fault and storm correlation to map outages in real time
  
  
  

• AI-based ETR prediction models for more accurate restoration timelines
  
  
  

• Crew dispatch optimization using real-time geospatial data
  
  
  

• Auto-generated outage communications to improve customer transparency
  
  
  

Deployed on a cloud-native architecture, the system seamlessly integrated with existing outage management platforms and enabled faster decision-making during crisis events.

Impact – Faster Restoration and Improved Accuracy

The transformation delivered measurable improvements in both operational efficiency and customer satisfaction:

• 14% reduction in SAIDI (System Average Interruption Duration Index)
  
  
  

• 19-point improvement in ETR accuracy across major events
  
  
  

• 12% reduction in crew miles traveled through optimized dispatching
  
  
  

• Enhanced storm readiness and better communication with customers
  
  
  

By combining streaming analytics and AI optimization, the utility achieved smarter storm response, faster restoration, and more reliable service continuity.

Meta Description

Discover how a leading utility provider used Nuodata’s Maestro platform to enhance outage intelligence—reducing SAIDI by 14%, improving ETR accuracy by 19 points, and cutting crew miles by 12% with AI-driven optimization.