An intelligent system for energy management in smart homes based on

Abstract

The increasing demand for energy efficiency and sustainability in smart home environments has spurred the development of advanced energy management systems (EMS). This thesis proposes a novel approach integrating multi-agent reinforcement learning (MARL) with fuzzy logic for efficient energy management in smart homes. The system employs a distributed architecture where autonomous agents interact with various smart devices to optimize energy consumption while considering user preferences and comfort levels. The use of MARL enables the system to adapt and learn from dynamic environments, allowing for real-time decision-making and optimization of energy usage. Each agent operates independently, yet collaboratively, to achieve the overarching goal of minimizing energy consumption and costs while maintaining user comfort. Fuzzy logic is incorporated to handle uncertainties and imprecise data inherent in smart home environments, providing robustness and flexibility to the decision-making process. The proposed system demonstrates significant improvements in energy efficiency compared to traditional approaches. Furthermore, the integration of fuzzy logic enhances the system's ability to handle complex and uncertain environments, resulting in more reliable and adaptive energy management solutions for smart homes. This research contributes to advancing the field of smart home automation by offering a scalable and intelligent energy management system capable of optimizing energy usage while ensuring user satisfaction and comfort.