Performance Comparison of Ant Colony Optimization and Symbiotic Organism Search In Smart Home Energy Management Systems

Home Energy Management Systems (HEMS) technology is increasingly being adopted due to its numerous benefits. This technology aims to facilitate the automatic operation and management of household appliances. Demand-side energy management seeks to manage and optimize energy usage and reduce electricity costs. Numerous approaches to optimizing demand-side energy management in HEMS have been proposed, utilizing linear programming, mixed integer linear programming, mixed integer nonlinear programming, dynamic programming, and convex programming. However, these approaches often struggle to handle numerous variables, such as a large number of appliances or loads. The application of metaheuristic algorithms in HEMS is proposed as a solution to this issue. The algorithms proposed in this study are Ant Colony Optimization and Symbiotic Organism Search. The resulting optimal scheduling recommendations are then analyzed and compared, focusing on load shifting schedules and cost savings. The Ant Colony Optimization algorithm can reduce costs by up to 5.27%, whereas the Symbiotic Organism Search algorithm can achieve reductions of up to 24.75%. After analysis and comparison, conclusions are drawn from this research.