Edge AI for Energy Systems
A five-day intensive that takes participants from edge computing fundamentals to production deployment of AI models on energy infrastructure. Covers the full stack: from signal decomposition for non-intrusive load monitoring to fleet management of edge devices at scale. Participants leave with hands-on experience on Jetson hardware and a development kit they keep.
Curriculum
What You'll Learn
Edge Computing Fundamentals
- Edge vs cloud architectures
- Latency requirements in energy
- Hardware selection (Jetson, Coral, custom)
Non-Intrusive Load Monitoring
- Signal decomposition techniques
- Appliance-level disaggregation
- Real-time NILM deployment
Smart Metering & Demand Response
- AMI data pipelines
- Demand response algorithms
- Consumer behavior modeling
Production Deployment
- Model optimization for edge devices
- OTA update strategies
- Fleet management at scale
IoT Security for Energy
- Threat modeling for edge devices
- Secure boot and attestation
- Data integrity in metering
Audience
Who Should Attend
IoT/embedded engineers
GCC data science teams
Energy technology managers
Smart grid architects
Delivery Format
Format
5-day in-person intensive.
Prerequisites
Python proficiency, basic ML knowledge.
What's Included
Jetson development kit (to keep), lab access, certificate.
Instructor
Dr. Sayonsom Chanda
Founder, SARAL
Former researcher at NREL and Idaho National Lab. R&D 100 Award recipient. Leads SARAL's research across Energy, AI, Quantum, and Robotics.