WattWise AI is a production-grade B2B SaaS web application designed for Indian industries to detect energy waste and optimize consumption patterns using artificial intelligence. The system provides real-time analytics, anomaly detection, and AI-powered recommendations to reduce energy costs by up to 30%.

Indian industries face significant challenges in energy management:

• High Energy Costs: Industrial electricity rates averaging ₹8.50/kWh
• Inefficient Consumption: Lack of visibility into energy usage patterns
• Manual Monitoring: Time-intensive manual analysis of energy data
• Compliance Requirements: Need for sustainability reporting and energy efficiency standards

WattWise AI provides:

• Automated Anomaly Detection: Statistical analysis to identify energy spikes and waste
• AI-Powered Insights: Gemini 3 integration for intelligent recommendations
• Cost Analysis: Real-time cost calculations in Indian Rupees (INR)
• Department-wise Analytics: Granular consumption tracking by department and equipment
• Actionable Recommendations: Practical steps to reduce energy consumption

• Energy Analytics Engine: Rule-based and statistical anomaly detection using scikit-learn
• Database: SQLAlchemy with SQLite for development, PostgreSQL for production
• AI Integration: Google Gemini 3 for intelligent insights
• API Design: RESTful endpoints with proper authentication and validation
• Authentication: Flask-Login with secure session management

• Professional Dashboard: Enterprise-grade UI with Chart.js visualizations
• Authentication System: Secure user registration and login with CSRF protection
• Data Visualization: Interactive charts showing energy trends and patterns
• Responsive Design: Mobile-friendly interface optimized for all devices

1. CSV Upload: Historical energy data from enterprise systems
2. Data Processing: Validation, parsing, and cost calculation at ₹8.50/kWh
3. Analytics: Anomaly detection using Isolation Forest and statistical methods
4. AI Analysis: Gemini 3 generates insights and recommendations
5. Dashboard: Visual presentation of findings and KPIs

The system accepts CSV files with the following structure:

timestamp,energy_kwh,department,equipment,building
2024-01-15 08:00:00,125.5,Production,Injection-Molding-Machine-A,Factory-1
2024-01-15 09:00:00,142.8,Production,Injection-Molding-Machine-A,Factory-1
2024-01-15 10:00:00,98.3,HVAC,Cooling-System,Main-Office

Required Columns:

• timestamp: Date and time (YYYY-MM-DD HH:MM:SS)
• energy_kwh: Energy consumption in kilowatt-hours (numeric)
• department: Department or area name (text)
• equipment: Equipment or source type (text)

Optional Columns:

• building: Building or facility name (text)

While this system uses CSV upload for demonstration and historical data analysis, production deployments would integrate with:

• Smart Meters: Real-time data via IoT sensors and APIs
• Building Management Systems (BMS): Direct API integration
• SCADA Systems: Industrial control system data feeds
• Energy Management Platforms: Third-party system APIs
• Enterprise Resource Planning (ERP): Integration with business systems

• Baseline Analysis: Calculate average consumption and deviation thresholds
• Anomaly Detection: Identify consumption spikes using Isolation Forest and statistical methods
• Pattern Recognition: Hourly, daily, and departmental usage patterns
• Cost Calculation: Automatic cost computation at ₹8.50/kWh (Indian industrial rate)

• Root Cause Analysis: Explain reasons for energy spikes in business-friendly language
• Optimization Recommendations: Actionable steps to reduce consumption
• Business Language: Plain English explanations for management
• ROI Focus: Cost savings and sustainability benefits in INR

• KPI Overview: Total consumption, costs, anomalies, peak hours
• Interactive Charts: Energy consumption trends using Chart.js
• Department Analysis: Consumption breakdown by department
• Equipment Tracking: High-consuming equipment identification
• AI Analysis Panel: Professional energy consulting reports

• Python 3.8+
• Node.js 14+ (for development tools)
• Google Gemini API key

# Clone the repository
git clone <repository-url>
cd wattwise-ai

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Initialize database
flask db init
flask db migrate -m "Initial migration"
flask db upgrade

# Run the application
python run.py

Create a .env file based on .env.example:

SECRET_KEY=your-secret-key-here
GEMINI_API_KEY=your-gemini-api-key-here
DATABASE_URL=sqlite:///wattwise.db
INDUSTRIAL_ENERGY_RATE=8.50

# Set production environment
export FLASK_ENV=production

# Use Gunicorn for production
pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 run:app

# Or use Docker
docker build -t wattwise-ai .
docker run -p 5000:5000 wattwise-ai

The project includes realistic sample datasets in sample_data/:

1. manufacturing_energy_data.csv: Manufacturing plant with production equipment, HVAC, lighting
2. it_company_energy_data.csv: IT company with data centers, office systems, network equipment

Both datasets contain:

• Realistic Indian industrial consumption patterns
• Energy spikes and anomalies for testing
• Department and equipment breakdown
• Timestamps covering multiple days

• Energy Reduction: 20-30% consumption decrease through optimization
• Operational Efficiency: Automated monitoring reduces manual effort by 80%
• Predictive Maintenance: Early detection of equipment inefficiencies
• ROI: Typical payback period of 6-12 months

• Energy Audits: Automated reporting for regulatory compliance
• Carbon Footprint: Reduced emissions through optimized consumption
• Sustainability Goals: Support for corporate environmental initiatives
• ESG Reporting: Data for environmental, social, and governance reports

• Multi-facility: Support for multiple plant locations
• Department Granularity: Detailed breakdown by operational areas
• Equipment Monitoring: Individual machine-level tracking
• User Management: Role-based access control

Backend:

• Flask 3.0.0 with modular blueprint architecture
• SQLAlchemy 3.1.1 for database ORM
• Flask-Login for authentication
• Pandas 2.1.4 for data processing
• Scikit-learn 1.3.2 for machine learning
• Google Generative AI (Gemini 3)
• Plotly 5.17.0 for charts

Frontend:

• Bootstrap 5.3.0 for responsive design
• Chart.js for data visualization
• Font Awesome 6.4.0 for icons
• Custom JavaScript with ES6 features
• CSS3 with modern design patterns

Database:

• SQLite for development
• PostgreSQL for production
• SQLAlchemy migrations for schema management

• Authentication: Secure login/logout with session management
• CSRF Protection: Flask-WTF for form security
• Input Validation: Comprehensive CSV format and data validation
• Password Security: bcrypt hashing for user passwords
• Session Security: Secure cookie configuration
• Error Handling: Comprehensive error management without information leakage

• POST /auth/register - User registration
• POST /auth/login - User login
• POST /auth/logout - User logout

• POST /upload - Upload energy data CSV
• GET /dashboard - Main dashboard view
• GET /insights - Energy insights list
• GET /ai-analysis - AI analysis for specific insight

• GET /api/energy-stats - Energy statistics for dashboard
• GET /api/analyze-insight/<id> - AI analysis for insight

# Run unit tests
python -m pytest tests/

# Run with coverage
python -m pytest --cov=app tests/

# Test CSV upload functionality
python -m pytest tests/test_upload.py

• Application Logging: Structured logging with different levels
• Error Tracking: Comprehensive error reporting
• Performance Monitoring: Request timing and database query monitoring
• User Activity: Audit trail for important actions

1. Real-time Integration: IoT sensor connectivity and live data streaming
2. Machine Learning: Predictive analytics for consumption forecasting
3. Mobile App: Native mobile application for facility managers
4. Advanced Reporting: Automated report generation and scheduling
5. Multi-tenant: Support for multiple companies on single platform
6. Integration Marketplace: Pre-built connectors for popular energy systems

For technical support or feature requests:

• Comprehensive error messages and validation feedback
• Sample data for testing and onboarding
• AI-powered recommendations through the analysis interface
• Detailed API documentation

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

This project is designed as an enterprise solution for Indian industrial energy optimization. Contact for licensing and deployment information.

WattWise AI - Empowering Indian industries with intelligent energy management.