The IoT Street Light Energy Tracker is an AI + IoT project that combines Billing estimator and AI usage prediction, built with Relay module. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on AI usage prediction and Relay module, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through Billing estimator, orchestrated with Relay module and AI usage prediction. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
Relay module
Current sensor (CT)
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
ESP32 / Arduino
Voltage sensor
Modular IoT pipeline around Billing estimator and AI usage prediction
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through Billing estimator; the connectivity and AI layer processes it with Relay module and AI usage prediction; and the output layer monitors and presents results via Usage dashboard. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with Billing estimator.
Implement connectivity and AI processing using Relay module and AI usage prediction.
Add the dashboard and alert layer via Usage dashboard.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply AI energy forecasting and Detecting usage anomalies
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The IoT Street Light Energy Tracker delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
