Enhancing Smart Home Energy Efficiency Using a Hybrid Genetic Algorithm and Improved Dandelion Optimizer

Rapid growth in electronic devices and smart appliances has significantly increased household energy consumption, peak load demand, and electricity costs. Enhancing energy efficiency in smart homes is, therefore, a critical challenge for both sustainability and affordability. This paper proposes a novel Hybrid Genetic Algorithm and Improved Dandelion Optimizer (HGAIDO) framework that intelligently schedules and manages household appliances integrated with photovoltaic (PV) systems. Unlike conventional metaheuristics, HGAIDO leverages the global search capability of Genetic Algorithms and the exploitation strength of the Improved Dandelion Optimizer, enhanced with gamma distribution, to achieve superior convergence and optimization performance. Extensive MATLAB simulations demonstrate that the proposed HGAIDO reduces energy consumption by 16.3% (171.5→143.5 kW) and lowers electricity costs by 26.2% (153.8→113.5 Rs) compared to unscheduled usage. Moreover, the Peak-to-Average Ratio is reduced from 4.3382 to 0.94461, highlighting improved load balancing and reduced grid dependency. HGAIDO outperforms standalone GA and DO algorithms in terms of convergence speed, execution time, and complexity, while ensuring high user comfort and adaptability. The findings confirm that integrating HGAIDO with IoT-enabled smart homes and PV systems not only minimizes energy consumption and costs but also contributes to sustainable living. This innovative hybrid optimization framework offers a scalable, cost-effective, and environmentally conscious solution for the next generation of smart home energy management.