AI-Driven IoT Framework for Optimal Energy Management in Consumer Devices

The explosion of Internet of Things (IoT) devices in consumer applications has led to an unprecedented demand for efficient energy management strategies. However, the existing approaches lack intelligence and responsiveness to varying user needs and grid conditions. This research study introduces an innovative AI-driven IoT framework to optimize energy consumption in consumer devices. The proposed framework dynamically adapts energy usage based on a device context, user behavior, and environmental conditions. The proposed framework integrates cutting-edge AI models and algorithms to analyze real-time data streams from IoT devices. In addition to that, the framework integrates real-time energy consumption data from connected devices with AI algorithms for demand forecasting, anomaly detection, and dynamic control. Further, the components and technologies employed illustrate how machine learning enhances decision-making processes for optimal energy efficiency. Experiments in simulated and real-world environments demonstrate significant energy savings of up to 20% compared to conventional methods. The proposed framework offers a scalable and adaptable solution for promoting sustainable energy utilization in the field of consumer electronics.