Revolutionizing energy management through AI-driven insights and community-powered sustainability
EcoNexus is an intelligent web platform complementing our mobile application that empowers users to:
- Reduce energy consumption by up to 25% through behavioral changes
- Participate in community-driven sustainability initiatives
- Optimize energy usage with real-time AI recommendations
- Track environmental impact with advanced visualization tools
- Node.js v18+
- npm or yarn
# Clone repository
git clone https://github.com/Ayushdevx/Econexusx
# Install dependencies
cd Econexusx && npm install
# Configure environment
cp .env.example .env
# Launch development server
npm run devApplication available at: http://localhost:5173/
- Real-time tracking with dynamic Recharts visualizations
- Anomaly detection using TensorFlow.js models
- Predictive analytics with LSTM networks for consumption forecasting
- Personalized benchmarking against regional averages
- AI-generated recommendations (heating optimization, appliance scheduling)
- Micro-challenges with progress tracking
- ROI calculator for energy upgrades
- Weather-adaptive optimization (API integration)
- Neighborhood leaderboards with gamification
- Group challenge system with impact aggregation
- Event coordination platform
- Success story sharing network
- IoT Integration: Smart device control via REST API
- Edge Computing: Local ML inference for offline use
- Carbon Accounting: Blockchain-verified impact records
- AR Visualization: 3D energy flow representation
graph LR
A[Real-time Sensor Data] --> B[Time-series Preprocessing]
B --> C[TensorFlow.js LSTM Model]
C --> D[Energy Forecast]
D --> E[Optimization Engine]
E --> F[Personalized Recommendations]
-
TensorFlow.js Models:
- Anomaly detection (Autoencoder architecture)
- Consumption forecasting (LSTM networks)
- Pattern recognition (CNN for appliance identification)
-
NLP Integration:
- Gemini 1.5 Pro chatbot for user interaction
- Context-aware query processing
-
Optimization Algorithms:
- Reinforcement learning for dynamic load balancing
- Genetic algorithms for multi-objective optimization
| Metric | Individual Impact | Community Impact |
|---|---|---|
| Annual CO2 Reduction | 1.2 tons | 50+ tons/neighborhood |
| Energy Savings | 15-25% | 30-40% peak reduction |
| Equivalent Trees | 60 trees/year | 2,500 trees/year |
Companion app features:
- On-the-go energy monitoring
- Push notification alerts
- AR energy audit tool
- Voice-controlled interface
| Category | Technologies |
|---|---|
| 🖥️ Frontend | React, TypeScript, Vite |
| 📊 Visualization | Recharts, Three.js |
| 🤖 Machine Learning | TensorFlow.js, Python |
| ⚛️ State Management | Zustand, Context API |
| ☁️ Infrastructure | AWS EC2, Docker |
| 🔒 Security | OAuth2, AES-256 |
Arunima Sakharkar
Rasika Gupta
Ayush Upadhyay
Shreyas Kumar
Explore our platform:
Live Demo | API Docs
This project is MIT licensed. See LICENSE for details.
Built with ❤️ by Team Code Red | GitHub Repo | Report Issues
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This version features:
- Enhanced visual hierarchy with strategic use of emojis and spacing
- Clear technical specifications in tabular format
- Mermaid diagram for AI architecture visualization
- Responsive design elements
- Prominent call-to-action sections
- Quantified impact metrics
- Consistent branding and color scheme
- Mobile-friendly structure
- Professional team presentation
The structure balances technical depth with approachable language, making it suitable for both developers and non-technical stakeholders.