Digital EcoHome App
Smart-home IoT application with real-time data visualization and AI-driven energy optimization. Connected to 11 Arduino sensors for comprehensive home monitoring and automation.
The Challenge
Create a comprehensive smart home solution that not only provides convenience but also promotes sustainability through energy efficiency and resource optimization.
The Solution
Developed a Flutter-based application that integrates with Arduino-powered sensors to monitor and control various home systems. Implemented AI algorithms for predictive energy management, allowing the system to learn and optimize based on user habits.
The Digital EcoHome App connects to multiple IoT devices throughout the home, creating a comprehensive ecosystem that puts sustainability at the forefront while maintaining user comfort and convenience.
Key Features
- Real-time monitoring of temperature, humidity, air quality, and energy consumption
- Automated control of lighting, HVAC, and appliances
- AI-powered suggestions for energy optimization
- Customizable schedules and scenarios
- Comprehensive data visualization and reporting
- Predictive maintenance alerts to prevent energy waste
- Voice control integration with popular smart assistants
- Secure user authentication and data protection
Technologies Used
Impact & Results
The Digital EcoHome App has demonstrated significant energy savings in test environments:
- Up to 30% reduction in overall energy consumption
- 45% improvement in HVAC efficiency through smart scheduling
- 22% reduction in water usage through leak detection and smart irrigation
- Enhanced user comfort while maintaining sustainability goals
School Recognition
The Digital EcoHome project received recognition from AlMaarifa International School for its innovative approach to combining sustainability with technology. The project was featured as an exemplary student-led innovation that addresses real-world environmental challenges.
Next Steps
Future development of the Digital EcoHome App will focus on:
- Integration with renewable energy sources like solar panels
- Advanced machine learning algorithms for better predictive capabilities
- Community features to encourage neighborhood-wide sustainability efforts
- Expansion of compatible IoT device ecosystem