RoomIQ - Intelligent Energy Control System
- Tech Stack: Python, OpenCV, Raspberry Pi, Arduino, ESP/nRF24L01+, Firebase/GitHub Pages
- Repository Access: Request Access via Email
RoomIQ is a decentralized, AI-driven classroom energy automation system that uses real-time people detection to intelligently control lights and fans. Designed for institutions, each room operates independently using a Raspberry Pi and camera, ensuring optimized energy usage without a central processor dependency.
The system wirelessly controls multiple switchboards through Arduino-Relay modules and adapts its behaviour based on occupancy, room zones, seasonal mode, and threshold timings. A cloud dashboard enables admins and faculty to remotely manage rooms, view logs, and update configurations.
- Real-Time People Detection - camera-based occupancy & zone detection
- Automated Controls - lights/fans turn ON/OFF based on smart timers
- Decentralized System - each room has its own Pi for processing & storage
- Wireless Switching - Arduino + Relay control for multiple boards
- Cloud Dashboard - thresholds, seasonal modes, room mapping, logs
- Role-Based Access - admin & faculty levels, secure and identity-based
- Fail-Safe Logic - auto resume after power failure with local backup
RoomIQ is a scalable and modular system, ideal for educational institutions seeking energy-efficient automation. Future enhancements include timetable integration, QR/NFC-based faculty entry, AC control, and a mobile app for real-time monitoring.
Technical approach
RoomIQ employs YOLOv8 or optimized OpenCV detection models for real-time human presence analysis. The system uses a decentralized architecture where edge nodes (Raspberry Pi) process video streams locally to maintain privacy and reduce latency. Communication between nodes and switchboards is handled via nRF24L01+ or ESP8266 modules using a custom mesh-like protocol. The backend is integrated with Firebase for real-time data logging and configuration management.