Abstract
The rising global energy demand has led to the adoption of the Internet of Things (IoT)-enabled smart home appliances that participate in demand response (DR) programs to optimize energy consumption and reduce costs. However, designing an effective energy management system (EMS) remains challenging due to the dynamic nature of electricity pricing and the need to balance user comfort (UC) with cost efficiency. This study presents a novel pricing-based EMS for IoT-enabled smart homes, utilizing the Eel and Grouper Optimizer (EGO) to enhance cost-effectiveness. The main goal is to reduce the peak-to-average ratio (PAR) and maximize electricity price savings while maintaining an optimal balance between energy consumption and UC. The EGO efficiently optimizes the power consumption of smart appliances in IoT-enabled smart homes. The proposed methods performance is excluded in the matrix laboratory, working platform and compared with various existing methods, including the Fire Hawk Optimizer, Arithmetic Optimization (AO), and Flying Foxes Optimization.The proposed strategy demonstrates a low computation time of 90 s, a low PAR of 1.5, and a reduced electricity cost of 4.3 cents, outperforming the existing methods. The EGO algorithm effectively optimizes cost-efficient energy management in IoT-based smart homes by minimizing the PAR and enhancing sustainability, reliability, and intelligent operation.
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