Title: Designing Self-Sustainable and Energy-Efficient Smart Homes Using IoT Systems Integrated with Renewable Energy Harvesting and Predictive AI Technologies
Authors: Emmanuel Augustine Etukudoh, Thompson Odion Igunma
Volume: 9
Issue: 4
Pages: 163-171
Publication Date: 2025/04/28
Abstract:
This paper explores the design and implementation of self-sustainable and energy-efficient smart homes through the integration of Internet of Things (IoT) systems, renewable energy harvesting, and predictive artificial intelligence (AI) technologies. The study begins with a detailed examination of IoT's role in automating smart homes, highlighting key components such as sensors and smart meters that facilitate real-time monitoring and control. It then delves into various renewable energy sources suitable for smart homes, including solar, wind, and geothermal energy, and discusses techniques for effectively harvesting and integrating these energy sources into smart home infrastructure. The paper further investigates the application of predictive AI technologies in optimizing energy consumption, presenting advanced algorithms and real-world examples of AI-driven energy management systems. The conclusion summarizes the key points, underscores the implications for future smart home design, and offers recommendations for homeowners, developers, and policymakers to enhance the adoption and efficiency of these technologies. By leveraging IoT, renewable energy, and AI, this paper envisions a transformative approach to creating smart homes that are not only technologically advanced but also sustainable and energy-efficient.