🌱 GreenPulse: Smart Energy Management Platform

An AI-powered energy management system for campuses that provides real-time monitoring, analytics, and optimization recommendations through an intelligent dashboard.

📋 Table of Contents

Overview
Features
Tech Stack
Installation & Setup
Usage
Project Architecture
API Documentation
Deployment
Known Issues
Future Work
Contributing
License
Author

🧠 Overview

GreenPulse addresses the critical need for intelligent energy management in campus environments. With rising energy costs and environmental concerns, educational institutions require sophisticated tools to monitor, analyze, and optimize their energy consumption patterns.

Problem Statement: Campus energy management systems are often fragmented, reactive, and lack predictive capabilities, leading to inefficient energy usage and increased operational costs.

Our Solution: GreenPulse provides a unified platform with real-time monitoring, AI-powered analytics, and actionable insights to optimize campus energy consumption and reduce environmental impact.

Context: This project was developed as a hackathon prototype to demonstrate modern web development practices and smart energy management concepts.

✨ Features

🎯 Core Functionality

📊 Real-time Energy Dashboard - Live monitoring of campus energy consumption
🏢 Multi-Building Management - Monitor multiple campus buildings simultaneously
📈 Interactive Analytics - Visual charts and graphs for energy patterns
🤖 AI-Powered Insights - Smart recommendations for energy optimization
🔄 WebSocket Integration - Real-time data updates without page refresh
📱 Responsive Design - Works seamlessly across desktop and mobile devices

🎨 UI/UX Features

🌟 Glassmorphism Design - Modern, elegant user interface
🌙 Dark/Light Theme - Customizable viewing experience
📊 Interactive Charts - Built with Chart.js for engaging data visualization
🎯 Intuitive Navigation - User-friendly interface design

🚀 Technical Features

⚡ High Performance - Fast API responses and optimized frontend
🔐 Secure Architecture - CORS protection and secure API endpoints
🐳 Containerized Deployment - Docker support for easy deployment
☁️ Cloud Ready - AWS deployment configurations included
📝 API Documentation - Auto-generated OpenAPI/Swagger docs

🛠️ Tech Stack

Frontend

Framework: Angular 17
Language: TypeScript
Styling: SCSS with Glassmorphism effects
Charts: Chart.js
HTTP Client: Angular HttpClient
Real-time: WebSocket integration

Backend

Framework: FastAPI (Python)
Database: PostgreSQL with TimescaleDB
Caching: Redis
Authentication: JWT tokens
WebSockets: Native FastAPI WebSocket support
Validation: Pydantic models

Infrastructure & DevOps

Containerization: Docker & Docker Compose
Cloud Platform: AWS (EC2, RDS, S3, CloudFront)
Web Server: Nginx (reverse proxy)
Process Management: Uvicorn with Gunicorn
Monitoring: Built-in health checks

Data & Analytics

Data Processing: Pandas, NumPy
Machine Learning: Scikit-learn
Time Series: TimescaleDB extensions
Data Format: ASHRAE Building Performance Dataset

🚀 Installation & Setup

Prerequisites

Node.js (v18 or higher)
Python (3.7 or higher)
PostgreSQL (12 or higher)
Docker (optional, for containerized setup)

Quick Start (Local Development)

Clone the repository

git clone https://github.com/surajsk2003/GreenPulse.git
cd GreenPulse

Backend Setup

cd backend
pip install -r requirements.txt

# Set environment variables
cp .env.example .env
# Edit .env with your database credentials

# Run the backend
python -m app.main

Frontend Setup

cd frontend/greenpulse-dashboard
npm install

# Start development server
ng serve

Database Setup (Optional)

# Using Docker
docker-compose up -d db redis

# Or set up PostgreSQL manually and create database
createdb greenpulse

🐳 Docker Setup (Recommended)

# Build and run all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Access the application:

📖 Usage

Dashboard Navigation

Building Selection
- Choose from available campus buildings
- View building-specific energy metrics
Real-time Monitoring
- Monitor live energy consumption
- Track cost and carbon emissions
- View efficiency scores
Analytics & Insights
- Analyze historical energy patterns
- View optimization recommendations
- Compare building performance
Leaderboard
- See building efficiency rankings
- Track improvement metrics
- Identify top performers

API Integration

The REST API provides programmatic access to all features:

# Get building list
curl http://localhost:8000/api/buildings

# Get real-time analytics
curl http://localhost:8000/api/analytics/campus/stats

# Access API documentation
open http://localhost:8000/docs

🏗️ Project Architecture

GreenPulse/
├── frontend/                    # Angular application
│   └── greenpulse-dashboard/
│       ├── src/
│       │   ├── app/
│       │   │   ├── components/  # Angular components
│       │   │   └── services/    # HTTP services
│       │   └── environments/    # Environment configs
│       └── package.json
├── backend/                     # FastAPI application
│   ├── app/
│   │   ├── core/               # Configuration & database
│   │   ├── routers/            # API route handlers
│   │   ├── models/             # Pydantic models
│   │   └── main.py             # Application entry point
│   └── requirements.txt
├── docker-config/              # Docker configurations
│   ├── docker-compose.yml
│   └── nginx.conf
├── ml-models/                  # Machine learning modules
├── ashrae-energy-data/         # Energy dataset
└── deploy-aws.sh              # AWS deployment script

System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │   Backend       │    │   Database      │
│   (Angular)     │◄──►│   (FastAPI)     │◄──►│  (PostgreSQL)   │
│   Port: 4200    │    │   Port: 8000    │    │   Port: 5432    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         │                       │                       │
    ┌─────────────────────────────────────────────────────────────┐
    │                     Docker Network                          │
    └─────────────────────────────────────────────────────────────┘

📚 API Documentation

The API follows RESTful principles and provides comprehensive documentation:

Core Endpoints

Method	Endpoint	Description
GET	`/`	API information
GET	`/health`	Health check
GET	`/docs`	Interactive API docs
GET	`/api/buildings`	List all buildings
GET	`/api/analytics/campus/stats`	Campus-wide statistics
GET	`/api/insights/recommendations`	AI recommendations
WebSocket	`/api/ws`	Real-time data stream

Response Format

All API responses follow a consistent format:

{
  "status": "success",
  "data": {
    // Actual response data
  },
  "total": 42,
  "message": "Optional message"
}

☁️ Deployment

AWS Cloud Deployment

The project includes automated AWS deployment with the following architecture:

Frontend: S3 Static Website + CloudFront CDN
Backend: EC2 instance with Nginx reverse proxy
Database: RDS PostgreSQL (optional)
Storage: S3 for static assets

# Configure AWS CLI
aws configure

# Deploy to AWS (update script with your credentials first)
chmod +x deploy-aws.sh
./deploy-aws.sh

Note: Update the deployment script with your specific AWS credentials and configuration before running.

Local Production Setup

# Build frontend for production
cd frontend/greenpulse-dashboard
ng build --configuration=production

# Run backend with Gunicorn
cd ../../backend
gunicorn app.main:app -w 4 -k uvicorn.workers.UvicornWorker

🧩 Known Issues

As this is a prototype, there are several known limitations:

Current Limitations

🔧 Database Integration: Some endpoints use mock data instead of live database
📊 ML Models: Machine learning features are placeholder implementations
🔐 Authentication: User authentication system not fully implemented
📱 Mobile Optimization: Some UI components need mobile refinement
🔄 Data Validation: Limited input validation on some forms

Mock Data Usage

Currently using mock data for:

Building energy readings
Historical analytics
ML model predictions
User management

🛠️ Future Work

Phase 1: Data Integration

Connect real energy monitoring hardware
Implement ASHRAE dataset processing
Add real-time data ingestion pipeline
Create data validation and cleaning workflows

Phase 2: AI/ML Enhancement

Develop energy consumption prediction models
Implement anomaly detection algorithms
Add optimization recommendation engine
Create automated reporting system

Phase 3: Production Features

User authentication and authorization
Multi-tenant campus support
Advanced dashboard customization
Mobile application development
Integration with campus management systems

Phase 4: Advanced Analytics

🤝 Contributing

Contributions are welcome! This project serves as a foundation for energy management solutions.

Development Workflow

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Code Style

Backend: Follow PEP 8 for Python code
Frontend: Use Angular style guide and TypeScript best practices
Comments: Document complex logic and API endpoints
Testing: Add tests for new features (when applicable)

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 Suraj Kumar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

👨‍💻 Author

Suraj Kumar
B.Tech, NITK Surathkal

🙏 Acknowledgments

ASHRAE: For providing the building performance dataset
FastAPI Community: For excellent documentation and support
Angular Team: For the robust frontend framework
Open Source Community: For the various libraries and tools used

⚠️ Prototype Disclaimer

This is a prototype/proof-of-concept developed for educational and demonstration purposes.

Not intended for production use without further development
Mock data is used in several components
Security features require enhancement for production deployment
Database connections and ML models need real-world calibration

For production deployment, additional work is required in security, scalability, and data integration.

🌱 Built with passion for sustainable energy management 🌍

If this project helped you or inspired your work, please consider giving it a ⭐

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
backend		backend
data-pipeline		data-pipeline
docker-config		docker-config
frontend/greenpulse-dashboard		frontend/greenpulse-dashboard
ml-models		ml-models
.dockerignore		.dockerignore
.gitignore		.gitignore
HACKATHON_PITCH.md		HACKATHON_PITCH.md
IMPLEMENTATION_ROADMAP.md		IMPLEMENTATION_ROADMAP.md
LICENSE		LICENSE
README.md		README.md
TECHNICAL_ARCHITECTURE.md		TECHNICAL_ARCHITECTURE.md
demo_data_generator.py		demo_data_generator.py
deploy-aws.sh		deploy-aws.sh
requirements.txt		requirements.txt
setup.sh		setup.sh

License

StrataMind/GreenPulse

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