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 dev

Application 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

Companion app features:

• On-the-go energy monitoring
• Push notification alerts
• AR energy audit tool
• Voice-controlled interface

Arunima Sakharkar

Rasika Gupta

Ayush Upadhyay

Shreyas Kumar

Explore our platform:

Live Demo | API Docs

This project is MIT licensed. See LICENSE for details.