Energy
Smart Grid Analytics
Edge-to-cloud analytics platform optimizing grid stability across 3.8GW of renewable capacity integration.
42%
Outage Reduction
99.97%
Grid Uptime
3.8GW
Renewable Capacity
850
Substations
42%
Outage Reduction
99.97%
Grid Uptime
3.8GW
Renewable Capacity
850
Substations
Measurable Results
Business Impact
Context
The Challenge
A regional utility needed to balance renewable integration and grid stability across aging infrastructure with limited real-time visibility into demand fluctuations.
“Renewable integration was destabilizing our grid. We needed predictive intelligence at the substation level—not centralized reporting after the fact.”
Aging Infrastructure
Decades-old substations with limited telemetry and no edge computing capability.
Manual Dispatch
Grid operators relied on experience-based decisions without predictive load forecasting.
SCADA Fragmentation
Multiple SCADA systems across service territories with no unified analytics layer.
Our Approach
The Solution
Deployed edge analytics on substations with centralized ML for load forecasting, outage prediction, and automated dispatch recommendations for grid operators.
Load Forecasting AI
Predictive models balancing renewable integration and grid stability across 850 substations.
Edge Analytics
Substation-level intelligence with real-time anomaly detection and automated alerting.
Operations Center
Unified grid operations dashboard with dispatch recommendations for operators.
Time-Series Platform
High-throughput SCADA and GIS data ingestion with millisecond-level query performance.
Cloud Infrastructure
Edge-to-cloud AWS architecture supporting 3.8GW renewable capacity integration.
System Design
Solution Architecture
Grid Operators
Operations Center
Edge Analytics
Load Forecast Models
Time-Series Store
Grid Intelligence
Delivery
Implementation Timeline
Discovery
Stakeholder alignment, data audit, and success criteria definition.
Architecture
Reference architecture, security model, and integration blueprint.
Model Training
Feature engineering, model development, and validation framework.
Deployment
Production rollout, observability, and enterprise integration.
Go Live
Full production launch, monitoring, and optimization handoff.
Engineering
Technology Stack
Python
PyTorch
AWS
SCADA
GIS
Docker
Edge Computing
Transformation
Business Outcomes
Before
- Reactive outage response
- Manual load balancing
- Limited renewable visibility
- Fragmented SCADA data
After
- Predictive outage prevention
- Automated dispatch AI
- Real-time renewable integration
- Unified grid analytics
“Edge analytics at the substation level changed everything. Outages dropped 42% and we integrated 3.8GW of renewables without compromising stability.”
