The Challenge
A regional utility company serving 2 million customers had deployed over 50,000 smart meters but lacked the infrastructure to extract meaningful insights from the data. They were collecting terabytes of meter readings but couldn't use it for anything beyond basic billing.
Leadership wanted to leverage this data for predictive maintenance, demand forecasting, and grid optimization, but their existing systems couldn't handle the volume or provide real-time analytics.
Our Approach
We designed and built a comprehensive data platform that could ingest, process, and analyze smart meter data in real-time, while also supporting historical analysis and machine learning workloads.
Phase 1: Data Infrastructure
- Built scalable data ingestion pipeline handling 500K+ events per second
- Implemented time-series database optimized for meter readings
- Created data lake architecture for historical analysis and ML training
- Established data quality monitoring and anomaly detection
Phase 2: Analytics Engine
- Developed real-time demand forecasting models using LSTM networks
- Built predictive maintenance algorithms for grid equipment
- Created consumption pattern analysis for rate optimization
- Implemented outage detection and automated alerting
Phase 3: Operational Dashboards
- Built real-time grid health monitoring dashboards
- Created executive reporting with key performance indicators
- Developed field crew mobile app for maintenance dispatch
- Integrated with existing SCADA and billing systems
Key Results
"We went from drowning in data to making decisions with confidence. The predictive maintenance alone has saved us millions in avoided equipment failures."- VP of Grid Operations, Regional Utility
Technology Stack
- Ingestion: Apache Kafka, AWS Kinesis for real-time streaming
- Storage: TimescaleDB for time-series, S3 for data lake
- Processing: Apache Spark, AWS Glue for batch processing
- ML/AI: Python, TensorFlow, Amazon SageMaker
- Visualization: Grafana, custom React dashboards
- Integration: Custom APIs, OSIsoft PI integration
Technical Highlights
Real-Time Anomaly Detection. ML models continuously analyze meter readings to detect unusual patterns that could indicate equipment failure, theft, or safety hazards - often before customers notice any issues.
Demand Forecasting. Our LSTM-based forecasting models predict demand with 95% accuracy up to 72 hours ahead, enabling optimized generation scheduling and reduced peak demand charges.
Grid Health Scoring. We developed a proprietary algorithm that combines multiple data sources to generate real-time health scores for grid segments, allowing proactive maintenance prioritization.
Sustainability Impact
The platform directly contributed to the utility's sustainability goals:
- Reduced energy waste by optimizing distribution efficiency
- Enabled better integration of renewable energy sources
- Supported dynamic pricing programs to shift demand to off-peak hours
- Provided data for carbon footprint tracking and reporting
Future Roadmap
The platform we built is now being extended to support electric vehicle charging optimization and distributed energy resource management, positioning the utility for the next generation of grid challenges.