Sustainable Smart Homes in Remote Areas Using IoT and AI Technologies

This research paper presents study on the design and implementation of smart homes in remote or off-grid areas using the Internet of Things (IoT) and Machine Learning (ML). In recent years, the growing demand for sustainable living solutions has emphasized the need for intelligent systems capable of addressing the unique challenges faced by geographically isolated or infrastructure-deficient communities. The proposed approach focuses on integrating real-time data acquisition and intelligent decision-making into the daily operations of homes powered by renewable energy sources. The system employs IoTbased sensors to monitor various environmental and operational parameters such as temperature, humidity, energy consumption, and appliance activity. These data streams are then analyzed using lightweight ML algorithms that enable predictive load forecasting, early fault detection, and user behavior modeling. This dual-layered approach allows for proactive energy management, improving the reliability and longevity of off-grid energy systems while enhancing user comfort. A case study involving a solar-powered prototype deployed in a rural setting demonstrates the feasibility and benefits of the proposed system. The results indicate a significant increase in energy efficiency and reduction in manual intervention, with the system dynamically adjusting operations based on predicted energy availability and user patterns. Furthermore, the implementation addresses several core challenges such as limited power, unreliable connectivity, and restricted technical support through robust, low-power designs and adaptive communication strategies. By merging IoT and ML technologies, the proposed smart home model contributes to building sustainable and intelligent living environments in remote locations. Future research will explore edge AI integration, scalable community-level networks, and the role of federated learning to enhance security and personalization.