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Abstract- The rapid expansion of smart residential environments requires integrated systems that combine security, energy optimization, and real-time monitoring within a unified framework. However, many existing solutions operate as isolated modules without embedded artificial intelligence for identity verification and dynamic energy management. This paper presents an AI-enabled smart home system that integrates vision-based authentication, occupancy-aware automation, and real-time energy analytics using an edge-computing architecture. The system is implemented on a Raspberry Pi 4 Model B and employs Haar Cascade-based face detection for owner authentication. Unauthorized access triggers automated email alerts to enhance security. Real-time electrical parameters, including voltage, current, and power consumption, are measured using a PZEM-004T, enabling continuous monitoring and automated monthly billing estimation. An occupancy-driven control mechanism using PIR sensing ensures automatic appliance shutdown during idle conditions, reducing energy wastage. A web-based dashboard provides live system monitoring. Experimental results demonstrate reliable intrusion detection and improved energy efficiency. The proposed framework offers a compact, scalable, and cost-effective solution for secure and sustainable smart home environments. Keywords: Smart Home System, Artificial Intelligence, Face Detection, Energy Monitoring, Occupancy-Based Automation, Edge Computing, Intrusion Detection, Energy Optimization, Embedded Systems.
Published in: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Volume 10, Issue 03, pp. 1-9
DOI: 10.55041/ijsrem58338