An AI Enabled Smart Home System with Energy Monitoring and Vision Security

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.