Optimizing Energy Efficiency in Smart Home Automation through Reinforcement Learning and Iot

Abstract

This thesis explores an innovative approach to optimizing energy efficiency in smart home environments by leveraging reinforcement learning (RL) and Internet of Things (IoT) technologies. As global energy demand rises and concerns over environmental sustainability intensify, smart homes offer a promising solution to reduce residential energy consumption while enhancing user comfort. The study presents a comprehensive architecture integrating IoT devices with RL algorithms, allowing for real-time monitoring and intelligent energy management. Through data collected from smart sensors, RL agents continuously learn and adapt to occupant behaviors and environmental changes, making optimal decisions to minimize energy usage without compromising user comfort. A real word-based analysis demonstrates that the proposed system achieves significant energy savings compared to traditional rule-based methods. The results underscore the effectiveness of combining RL and IoT for adaptive energy management, paving the way for scalable solutions that could extend to smart cities and renewable energy systems. This research provides valuable insights into how emerging technologies can contribute to sustainable energy practices in the residential sector.