An AI-Based Occupancy-Aware Smart Energy Management System for Classroom Automation Using Computer Vision and IOT

Energy efficiency has become a critical global concern due to rising electricity demand and inefficient utilization in institutional environments. Classrooms in educational institutions contribute significantly to energy wastage, as electrical systems such as lighting, fans, and air conditioning units are frequently left operational even in the absence of occupants. This paper proposes an AI-based occupancy-aware smart energy management system that integrates computer vision and Internet of Things (IoT) technologies to optimize classroom energy consumption. The system performs real-time occupancy detection using AI-based image processing techniques and dynamically controls electrical appliances through an ESP32-based IoT framework. Unlike traditional motion sensor or timer-based systems, the proposed model introduces zone-based occupancy analysis, enabling fine-grained energy control. Experimental evaluation demonstrates improved energy efficiency, reduced operational wastage, and fast response time. The system is cost-effective, scalable, and suitable for deployment in smart educational infrastructures, contributing to sustainable energy management and smart campus development