Contributed to a real-time energy monitoring system to predict power usage and detect anomalies (e.g., energy theft) in public infrastructure. 🔹 Forecasting Module: • Built ARIMA-based models using Python and statsmodels to predict power consumption trends. • Processed and analyzed real-time data from IoT devices (~3–4 lakh records) using Pandas and MySQL. • Worked with the backend team to integrate forecast results into APIs. 🔹 Anomaly Detection + Frontend Module: • Developed anomaly detection logic using statistical thresholds and energy behavior rules (e.g., sudden spike in current_ph1 without matching load). • Created a responsive frontend dashboard in React.js + Tailwind CSS to visualize power metrics, forecasts, and alerts. • Used Chart.js for graphs, Axios for API calls, and collaborated on UI testing for real-time data monitoring.