PROJECT 01 / 2026 — Present / In progress
Generative Digital Twin for Real-Time Energy Management
A real-time industrial energy-management platform using machine learning-based demand forecasting and autonomous load optimization.
Energy overview
CONCEPT VISUALIZATION Illustrative interface & sample data
Problem
Industrial energy management brings together equipment telemetry, changing demand and decisions about how energy is used. This project focuses on connecting those signals within a digital twin.
Solution
An AI-powered platform for real-time industrial monitoring and autonomous energy optimization, with a FastAPI backend connecting telemetry, equipment state and AI services.
Architecture
REST and WebSocket endpoints support telemetry ingestion, equipment synchronization and AI-service communication. MongoDB services store sensor data, energy metrics and predictive analytics.
- 01IoT telemetry & equipment
- 02FastAPI · REST + WebSockets
- 03MongoDB · asynchronous services
- 04AI/ML · predictive analytics
Technical Decisions
- Use REST and WebSockets for request-based operations and real-time updates.
- Build asynchronous MongoDB services and schemas for telemetry and energy metrics.
- Connect AI services to the backend for forecasting and optimization.
Key Features
- Telemetry ingestion
- Equipment synchronization
- Real-time WebSocket updates
- Predictive analytics
- Autonomous energy optimization in development
Challenges
The engineering focus is handling frequent sensor writes while keeping equipment updates responsive. Current load testing exercises the MongoDB services alongside the real-time API layer; the project remains in progress.
Outcomes
15+ endpoints implemented to date, sub-second push latency and 5,000+ writes per minute in current load testing. These are development-stage results, not a production SLA or a measured energy-saving claim.
- API endpoints implemented
- 15+
- WebSocket push latency
- < 1s
- writes / min · current load testing
- 5,000+