Fuzzy Logic Control System for Smart Home Energy Management

This paper offers an extensive examination of Fuzzy Logic Control (FLC) systems in smart home energy management, with emphasis on their use in dynamic and uncertain settings. FLC offers a strong framework for modeling control systems where inputs are uncertain and partially controlled by the system. The study explores how FLCs facilitate smart homes to optimize energy consumption through balancing short-term consumption with long-term energy saving, especially in situations with time- varying energy demands and uncertain conditions. The research adopts a Fuzzy Logic-based Smart Home Energy Management System (FLSHEMS), combining energy optimization methods with conventional FLC models. This integration enables the system to learn its energy management tactics based on experience and feedback from environmental conditions, like occupancy and energy usage patterns. The performance of the system is tested in different scenarios, like peak demand, energy storage, and real-time energy optimization, and measures like energy savings, cost-effectiveness, and user comfort are gauged. Results indicate dramatic improvements in energy efficiency, with energy savings rates holding above 70 percent even under dynamic energy loads. The system is especially robust in realtime adaptability, successfully adapting energy consumption during peak hours while keeping comfort and costs to a minimum. The implementation effectively balances computational efficiency with control quality, rendering it appropriate for real-world use. This work makes a contribution to smart home energy management by presenting an adaptive and flexible control framework, providing real-world solutions for energy optimization, and proving the efficacy of integrating fuzzy logic with contemporary optimization methods for enhanced smart home performance.