“Let’s follow a single data point in the life of a utility.
It begins at a smart meter in your home — recording how much power you’re using, minute by minute.
That reading doesn’t just sit there — it travels instantly through IoT networks and SCADA systems into the utility’s data lake, joining millions of other signals from transformers, weather stations, and even satellites.
Inside the data lake, AI models wake up. They predict tomorrow’s demand, flag unusual patterns that might mean energy theft, and even forecast which transformer could fail during the next storm.
From there, insights flow into dashboards in the control room. Operators see heat maps, outage predictions, and recommended crew routes. At the same time, customers see personalized tips on their mobile apps — like how to cut their bill or when power will be restored.
Finally, the loop closes: those decisions go back into the grid through ADMS and OMS systems, switching circuits, dispatching crews, and keeping the lights on.
One tiny data point, traveling across systems, becomes the heartbeat of a smarter, more resilient grid.”
Just think of it with 3 layered structures
Layer 1: Data Foundation & Integration (The foundation: Where all raw data is collected, cleaned, and stored.) consists of *Data Sources (The "Eyes & Ears" of the Grid), *Data Ingestion & Integration Layer (The "Data Highways"), *Data Lake & Storage (The "Data Reservoir")
Layer 2: AI Analytics & Decision Support (The intelligence hub: Where data is processed, analyzed, and transformed into actionable insights.) consists of *AI / ML Analytics Layer (The "Smart Brain"):, *Visualization & Decision Support (The "Control Panel")
Layer 3: Field Execution & Feedback Loop (The action-taker: Where insights lead to physical actions and continuous system improvement.) consists of *Field Execution & Feedback Loop (The "Action & Learning Cycle")
Data Sources (AMI, SCADA, IoT, Weather, GIS, ERP, Vegetation Imagery)
AMI (Smart Meters): AI detects abnormal consumption → theft detection or faulty meter alerts.
SCADA: ML predicts equipment overloads or abnormal voltage swings → triggers preventive maintenance.
IoT Sensors: AI monitors transformer oil/gas data → predicts failure before outage.
Weather Feeds: Storm impact prediction → tree-fall risk and flood-prone substations.
GIS + ERP: Geospatial AI suggests optimal placement for new assets (substations, EV chargers).
Vegetation Management (LiDAR & Drone Imagery): High-resolution LiDAR scans and orthomosaic maps → AI detects tree encroachment and computes vegetation-risk zones.
