Grid Operator Reduces Outage Duration 35% with Predictive AI
Combining SCADA, weather, and asset data on Azure to predict outages before they happen and accelerate restoration when they do.
Project Overview
The Challenge
Outages were detected reactively through customer calls, with restoration crews dispatched without good prediction of the root cause.
- 1Reactive outage detection via customer complaints
- 2Inefficient crew dispatch without root-cause prediction
- 3Long average restoration times eroding customer satisfaction
Our Solution
CloudGate built an outage prediction and response platform combining grid sensor data, weather feeds, and historic incident patterns.
- Predictive outage scoring at feeder and asset level
- Smart crew dispatch with predicted root cause and parts requirements
- Mobile field app for crews with maps, asset history, and live customer comms
The Results
- Predicted weather-induced outages 4–8 hours in advance with 75% accuracy
Technologies Used
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