Sustainable Smart Homes in Remote Areas Using IoT and AI Technologies

Abstract

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 IoT-based 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.

Introduction

The concept of smart homes has rapidly advanced due to IoT, Machine Learning (ML), and automation, primarily improving urban living by automating household tasks and optimizing energy use. However, smart home solutions remain underdeveloped for remote or off-grid areas, where infrastructure is limited and power supply is unreliable. These regions often rely on renewable energy sources like solar power but suffer inefficiencies without proper monitoring.

The integration of IoT sensors and ML algorithms offers a promising approach to build intelligent, self-sustaining homes that efficiently manage energy and appliances despite constraints such as low power and intermittent connectivity. The proposed system uses low-power IoT devices and edge computing to collect and process real-time data, employing ML models like LSTM for load forecasting, Isolation Forest for anomaly detection, and K-Means for usage patterns. This enables automated control, fault detection, and adaptive energy management to improve reliability and user experience.

The system architecture consists of four layers—sensing, edge processing, intelligent analytics, and user interface—ensuring low-latency operation and user engagement. Evaluations show accurate energy forecasting, effective anomaly detection, and significant daily energy savings (about 2.5 kWh). User satisfaction improved over time, indicating strong acceptance and usability.

Conclusion

This study has highlighted the transformative potential of integrating Internet of Things (IoT) and Machine Learning (ML) technologies in smart homes, especially in remote and off-grid areas where infrastructure is limited or nonexistent. The intelligent home energy management system proposed and evaluated in this paper presents a viable pathway toward achieving sustainable, efficient, and user-centric living environments in such contexts.By deploying IoT-enabled sensors and actuators, the system is capable of real-time monitoring of environmental conditions and appliance usage. Machine learning models, including LSTM for load forecasting, Isolation Forest for fault detection, and K-Means for behavioral segmentation, enable predictive and adaptive control strategies. These models collectively enhance decision-making capabilities, reduce unnecessary energy usage, and provide a safer and more responsive living experience. Our results show substantial improvements across multiple performance metrics. High prediction accuracy with low MAE and RMSE confirms the reliability of forecasting models. Similarly, the anomaly detection system demonstrates high precision and recall, significantly improving fault identification while minimizing false alarms. Most importantly, the tangible benefits in energy savings—both per household and at a community level—underline the economic and environmental potential of the system. User feedback further confirms satisfaction with system performance and usability, reinforcing the value of intelligent automation in improving quality of life. The successful implementation of the prototype system in a real-world rural setting demonstrates that such technology is not only technically feasible but also scalable. The feedback loop embedded in the architecture ensures that the system continues to learn and adapt, aligning with both environmental changes and evolving user preferences. This dynamic adaptability is particularly critical in off-grid regions, where unpredictability and lack of resources can otherwise hinder long-term sustainability.Looking ahead, future enhancements could include the incorporation of edge computing for faster local decision-making, integration with blockchain for secure energy transactions, and the use of federated learning to maintain data privacy while improving personalized experiences. Community-level microgrids and collaborative optimization among neighboring homes also present exciting avenues for expansion. In conclusion, the fusion of IoT and ML within the framework of smart homes offers a promising, impactful, and scalable solution to some of the most pressing challenges in remote habitation. This research lays a solid foundation for future explorations and implementations that can bridge the digital divide and support inclusive, sustainable development worldwide.

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Copyright

Copyright © 2025 Nidhi Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.