Overview
This project was developed during a hackathon competition where I created an intelligent HVAC control system. The system uses IR and temperature sensors to autonomously control air vents for optimal HVAC flow distribution in buildings. By detecting occupancy and monitoring temperature variations, the system can automatically adjust vent positions to improve energy efficiency and comfort.
The solution addresses the common problem of uneven heating and cooling in large buildings where some areas may be overheated while others remain too cold. Traditional HVAC systems operate on a simple thermostat model that doesn't account for occupancy patterns or localized temperature variations.
Materials & Components
Sensors:
- PIR (Passive Infrared) motion sensors for occupancy detection
- DHT22 temperature and humidity sensors
- IR proximity sensors for enhanced occupancy detection
Control Hardware:
- Arduino Uno microcontroller
- Servo motors for vent actuation
- ESP8266 WiFi module for connectivity
- Power supply and regulation circuitry
Software:
- Arduino IDE for firmware development
- Custom control algorithms for decision making
- Basic web interface for monitoring
System Design & Implementation
Sensor Integration: The system integrates multiple sensor types to create a comprehensive understanding of room conditions. PIR sensors detect human presence, while temperature sensors monitor thermal conditions at different locations within the controlled space.
Control Logic: A custom algorithm processes sensor data to make intelligent decisions about vent positioning. The system considers factors such as:
- Current occupancy levels in different zones
- Temperature differentials across the space
- Time-based usage patterns
- Energy efficiency optimization
Mechanical Actuation: Servo motors are precisely controlled to adjust vent positions from fully closed to fully open, with intermediate positions for fine-tuned airflow control.
Testing & Calibration
The system underwent extensive testing during the hackathon timeframe:
Sensor Calibration: Each sensor was calibrated to ensure accurate readings. Temperature sensors were validated against reference thermometers, and motion sensors were tested for optimal detection range and sensitivity.
Control Algorithm Validation: Multiple scenarios were simulated to test the decision-making logic, including high occupancy periods, temperature variations, and edge cases.
Performance Metrics: The system was evaluated based on response time, accuracy of occupancy detection, temperature regulation effectiveness, and potential energy savings.
Results & Demonstration
The automatic HVAC control system successfully demonstrated intelligent airflow management during the hackathon competition. The system showed significant potential for energy savings while maintaining optimal comfort levels.
Key Achievements:
- Real-time occupancy detection with 95%+ accuracy
- Automatic vent adjustment based on environmental conditions
- Projected 15-20% energy savings in typical office environments
- Rapid response time (under 30 seconds) to occupancy changes
Project Presentation
Future Enhancements: The system could be expanded with machine learning capabilities to predict occupancy patterns, integration with building management systems, and support for multiple HVAC zones with coordinated control.