Real-Time Intelligent Control Systems for Energy Efficiency in Smart Homes Using IoT Sensors

This study explores the optimization of real-time energy management in smart homes through an AI-driven control system that integrates Deep Deterministic Policy Gradient (DDPG) with Particle Swarm Optimization (PSO). The main goal is to improve energy efficiency by utilizing IoT sensors for continuous data collection, enabling the DDPG model to make adaptive and informed energy management decisions. PSO is used to optimize key hyperparameters within the DDPG framework, enhancing the model’s performance and precision. Experimental results reveal an average energy reduction of up to 25.2% compared to traditional baseline methods, highlighting the effectiveness of the proposed approach. These findings emphasize the potential of combining reinforcement learning with optimization techniques to achieve significant energy savings while preserving user comfort in smart home settings.