Intelligent residential load scheduling for smart home.

The residential load sector ensures power system stability and effective energy management. Despite efforts to integrate renewable energy sources and develop information and communication technology, the residential sector's flexibility, energy management, and scheduling challenges persist, impeding the power grid's stability and efficiency. Demand Side Management (DSM) has emerged as a crucial solution to address these challenges. This paper proposes a DSM model that employs Binary Particle Swarm Optimization to perform residential load scheduling while considering a time-of-use pricing scheme. Simulation results demonstrate a significant reduction in energy costs, validating the proposed model's effectiveness in enhancing the power grid's stability and efficiency. By adopting this approach, residential consumers can efficiently manage their energy consumption, reducing overall energy costs while contributing to the power grid's stability and efficiency. The proposed DSM model represents a significant step towards achieving a sustainable and cost-effective energy future, offering a compelling argument for its adoption in residential settings. This paper contributes to the field of energy management by providing a technically rigorous and persuasive analysis of the benefits of DSM in residential load scheduling. [ABSTRACT FROM AUTHOR]