Multi-objective optimization of residential energy scheduling using NSGA-II: A scenario-based weighted evaluation of thermal comfort, cost and environmental performance

This paper presents a multi-objective optimization framework for residential energy scheduling, employing the NSGA-II algorithm to efficiently balance economic, environmental, and comfort objectives. The primary innovation lies in the integrated approach that simultaneously optimizes electricity cost, total energy consumption, user comfort, peak demand, CO 2 emissions, and thermal comfort indices. results demonstrate significant improvements over the base scenario: a 52.7% reduction in electricity cost , a 51.8% decrease in peak demand , and a 49.1% lowering of total energy consumption . Furthermore, the framework achieved substantial CO 2 emission reductions while maintaining thermal comfort within acceptable ranges, as indicated by improved discomfort indices. This study offers a robust, data-driven solution for smart energy management in buildings, showcasing a practical trade-off between competing objectives and highlighting the potential for substantial energy savings and environmental benefits. The developed methodology provides a valuable decision-support tool for homeowners and grid operators seeking to enhance energy efficiency and sustainability.