🌾 Gram Meter

Bridging the Gap Between Grid and Grassroots
A Smart Energy Monitoring Platform for Rural India

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📖 Overview

Gram Meter is an enterprise-grade smart energy monitoring solution designed specifically for rural India. It empowers farmers, village leaders (Sarpanch), and utility providers with real-time insights into electricity consumption, quality, and efficiency.

By combining IoT data, Machine Learning, and Multilingual support, Gram Meter addresses critical challenges like voltage fluctuations, power theft, and inefficient usage patterns.

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🌟 Key Features

🔐 Enterprise-Grade Security

• Mobile-First Authentication: OTP-based login (no passwords required).
• Device Fingerprinting: Tracks user devices and prevents session hijacking.
• Role-Based Access Control (RBAC):
  • 👨‍🌾 Farmer: View personal consumption, bills, and alerts.
  • 🏛️ Sarpanch: Village-level overview and critical infrastructure monitoring.
  • ⚡ Utility Provider: Grid load balancing and theft detection.
  • 🇮🇳 Government: Policy planning and subsidy management.

📊 Advanced Analytics & ML

• Real-Time Monitoring: Live tracking of Voltage, Current, Power, and Frequency.
• Anomaly Detection: AI-powered detection of:
  • ⚡ Voltage Spikes/Drops
  • 🔌 Phantom Loads
  • ⚠️ Power Theft/Tampering
  • 🌑 Power Outages
• Smart Forecasting: 7-day energy consumption predictions using LSTM models.
• Efficiency Scoring: A-F grading system for energy usage with actionable recommendations.

🌍 Rural-Optimized Design

• Multilingual Interface: Full support for English, Hindi, and Gujarati.
• Offline Capabilities: Works reliably on low-bandwidth networks.
• Multi-Channel Alerts: Notifications via SMS, WhatsApp, and Email.

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🏗️ Architecture

The platform follows a modern microservices-inspired architecture:

• Backend Core: Django 6.0 (User management, Billing, Notifications).
• ML Gateway: FastAPI (High-performance inference for Forecasting & Anomalies).
• Frontend: React + Vite (Responsive, PWA-ready UI).
• Database: SQLite (Dev) / PostgreSQL (Prod).
• Real-Time Layer: Redis + Django Channels (WebSockets).

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 16+
• Redis (for WebSockets and Caching)

📥 Installation

1. Clone the Repository

   git clone https://github.com/Jaimin2687/gram-meter.git
   cd gram-meter

2. Setup Backend

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r backend/requirements.txt
   
   # Initialize Database
   cd backend
   python manage.py migrate
   python scripts/populate_sample_data.py  # Generates test users & data
   cd ..

3. Setup Frontend

   cd frontend
   npm install
   cd ..

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🏃‍♂️ How to Run

Option 1: One-Click Startup (Recommended)

We provide a unified script to start the Backend, Frontend, and Admin Console simultaneously.

Mac/Linux:

./start_platform.sh

Windows:

start_platform.bat

Option 2: Manual Startup

Terminal 1: Django Backend

source .venv/bin/activate
cd backend
python manage.py runserver

Terminal 2: Frontend Application

cd frontend
npm run dev

Terminal 3: ML Gateway (Optional)

source .venv/bin/activate
cd backend
uvicorn ml_gateway.main:app --reload --port 8001

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🧪 Demo Credentials

Use these accounts to explore different roles:

Role	Username	Password	Phone
Farmer	farmer_ramesh	password123	+917012345678
Sarpanch	sarpanch_kumar	password123	+919876543210

Note: In development mode, the OTP will be printed in the backend console.

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📡 API Documentation

The backend provides a comprehensive REST API. Once the server is running, access the interactive documentation:

• Swagger UI: http://localhost:8000/api/docs/
• ReDoc: http://localhost:8000/api/redoc/

Key Endpoints

• POST /api/v1/auth/login/ - Request OTP
• GET /api/v1/meters/ - List smart meters
• GET /api/v1/analytics/forecast/ - Get energy predictions
• POST /api/v1/analytics/ml/detect_anomaly/ - Real-time anomaly check

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📂 Project Structure

gram-meter/
├── backend/                 # Django Project Root
│   ├── analytics/           # ML & Data Analysis App
│   ├── billing/             # Invoicing & Payments
│   ├── core/                # User Auth & Role Management
│   ├── meters/              # Smart Meter Management
│   ├── ml_gateway/          # FastAPI Microservice
│   └── scripts/             # Data Generators & Utilities
├── frontend/                # React + Vite Application
│   ├── src/
│   │   ├── components/      # Reusable UI Components
│   │   ├── pages/           # Application Views
│   │   └── services/        # API Integration
├── ML/                      # Machine Learning Models (.pkl)
└── Admin console/           # Government/Utility Admin Panel

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🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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