We implemented an end-to-end Smart Grid AI Platform that transformed GreenGrid from a dumb pipe into an intelligent, self-optimizing energy network.
1. Hyper-Accurate Demand Forecasting
We built a deep learning model combining:
* Historical consumption patterns (5 years, 15-minute intervals)
* Granular weather data (temperature, cloud cover, wind speed, humidity)
* Calendar events (holidays, local events, school schedules)
* Real-time EV charging station data
* Satellite imagery for solar irradiance prediction
The model forecasted demand at 15-minute resolution, 48 hours ahead, with 97% accuracy (vs. 82% with legacy methods).
2. Renewable Output Prediction
A separate model predicted solar and wind generation output for each distributed asset:
* "Solar Farm Alpha will produce 12MW between 10AM-2PM (confidence: 94%)"
* "Residential solar in Zone 5 will drop 60% at 3PM due to incoming cloud cover"
This allowed the grid to pre-position conventional generation to fill predicted gaps.
3. Automated Grid Balancing
An optimization engine ran every 5 minutes, adjusting:
* Generator dispatch schedules (cheapest mix to meet demand)
* Battery storage charge/discharge cycles (store excess solar, discharge at peak)
* Demand response signals (asking commercial customers to reduce load during peaks in exchange for credits)
* Inter-utility power trading (buy cheap wind power from a neighboring utility at 2AM)
4. Predictive Maintenance for Grid Assets
Sensors on transformers, substations, and transmission lines fed data to anomaly detection models. The AI identified equipment degradation weeks before failure, preventing costly outages and extending asset life by 15%.
