Project Overview
ss.au developed a scalable energy grid solution for city infrastructure using AI-driven load balancing, distributed sensor networks, and real-time demand forecasting. The system optimizes power distribution across 250,000+ residential and commercial units.
- Timeline
- Dec 2023 - Jul 2025
- Team
- 8 Engineers, 2 Energy Consultants, 3 AI Researchers
Key Components
Dynamic Load Balancing
Real-time redistribution of energy across sub-grids using predictive analytics and machine learning patterns.
Fault Detection
Proactive grid monitoring with neural networks identifying potential system failures before they occur.
Demand Forecast
Weekly demand projections using weather data and historical patterns to prevent overloads during peak hours.
User Portal
Web application with visual dashboards showing energy usage, cost savings, and sustainability metrics.
Our Stack
IoT Edge Devices
Smart meters and sensors across the grid
TensorFlow Serving
For deployment of production models
Kafka Streams
Real-time analytics pipeline
AWS IoT Core
Device management and data ingestion
Results & Impact
Reduction in energy waste
Across all service areas
Improvement in fault detection
Faster resolution of grid issues
Sustainability savings
Equivalent CO2 reductions
The system has achieved industry leading efficiency metrics while maintaining 99.999% uptime for critical infrastructure.