An effective IoT-based demand response for energy-efficient smart homes

The proliferation of energy demand with population growth and associated costs necessitated the development of advanced demand response (DR) strategies in smart grid (SG) environments. This study proposes a novel IoT-enabled Energy Management Controller (IEMC) for smart buildings that addresses the critical challenge of optimal appliance scheduling. The proposed system integrates renewable energy sources (photovoltaic systems), energy storage systems (ESS), and advanced metering infrastructure (AMI) to enable autonomous energy management under Time-of-Use (ToU) pricing schemes. The study categorizes household appliances into schedulable and non-schedulable classes, implementing a hybrid metaheuristic optimization algorithm (HGPO) that combines Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Wind Driven Optimization (WDO) techniques. The multi-objective optimization framework simultaneously addresses four critical performance metrics: electricity cost minimization, peak-to-average ratio (PAR) reduction, carbon emission mitigation, and user comfort (UC) maximization. Extensive simulations demonstrate the superior performance of the proposed IEMC system. The hybrid HGPO algorithm achieves a 57.8% improvement in fitness cost (19.34) compared to traditional GA approaches (39.66), while maintaining the lowest emissions (3.41 tonnes/h) and optimal PAR (10). The system successfully shifts schedulable appliances from peak to off-peak hours, resulting in a 79% reduction in grid import dependency and enhanced battery state-of-charge management with peak utilization reaching 8%. Furthermore, comparative analysis with five other metaheuristic algorithms (GA, Binary PSO, WDO, Ant Colony Optimization, and Bacterial Foraging Algorithm) validates the superiority of the hybrid approach across all performance metrics.