Optimizing real-time demand response in smart homes through fuzzy-based energy management and control system

This paper introduces an innovative demand response energy management system tailored for smart homes, aimed at optimizing appliance usage in real time. The system considers dynamic pricing tariffs, device characteristics, usage patterns and user behavior to achieve efficient energy management. Unlike conventional systems, the proposed approach integrates a novel fuzzy logic-based pricing system that combines real-time pricing, multi-time pricing and load-dependent inclining block rate coefficients. This integration enhances cost reduction effectiveness for both homeowners and grid operators. Furthermore, appliance runtime optimization is achieved through linear programming, enhancing consumer behavior and domestic energy efficiency. By merging mathematical optimization methods with AI-enabled smart pricing coefficients, practical applications in real-world energy management scenarios are demonstrated. Moreover, a user-friendly interface is designed to facilitate real-time multitasking optimization steps using MATLAB, thus advancing the application of Internet of things (IoT) beyond data storage and communication to include intelligent real-time optimizations. The effectiveness of the proposed system is evaluated in various usage scenarios, including an analysis of the impact of comfort parameters and user behaviors. Additionally, savings effectiveness is compared with existing pricing systems. Results show that the proposed system optimizes energy usage effectively, leading to significant cost savings for consumers and improved grid management for operators. The analysis highlights the system's adaptability to various usage scenarios and its potential to enhance user comfort and energy efficiency, thus presenting a robust solution for demand response in residential settings.