Key Features Implemented:
- Real-time Data Simulation
Simulates 100 smart meters generating readings every 3 seconds (representing 15-min intervals) Realistic consumption patterns with morning/evening peaks and night lows Random anomaly injection (10% probability)
- Anomaly Detection
Zero Consumption: Possible meter bypass (HIGH severity) Sudden Drop: Potential tampering (HIGH severity) Unusual Spike: Load mismatch (LOW severity) Automatic severity classification and revenue loss estimation
- Interactive Dashboard Components
Stats Cards: Total meters, active anomalies, revenue at risk, avg consumption Real-time Consumption Chart: Live trend visualization Anomaly Distribution: Pie chart showing anomaly types Alerts Table: Detailed view of recent anomalies with severity levels
- Revenue Protection Features
Estimates monthly revenue loss for each anomaly Color-coded severity indicators (red/yellow/blue) Pending status tracking for field officer action
How to Use:
Click "Start Simulation" to begin real-time monitoring Watch as meters generate data and anomalies are detected automatically Monitor the consumption trend and anomaly distribution charts Review the alerts table for suspected theft cases Click "Stop Simulation" to pause monitoring
Technical Implementation:
Built with React and Recharts for visualization Implements time-based consumption patterns (peak hours modeling) Statistical anomaly detection algorithms Real-time data processing and visualization updates
This prototype demonstrates how DISCOMs can leverage smart meter data to detect electricity theft, protect revenue, and enable field officers to act quickly on suspicious cases. The system is scalable and ready to integrate with actual smart meter infrastructure!