Energy-Efficient IoT with Deep Learning: Optimizing Resource Allocation in Smart Grids

The integration of Internet of Things (IoT) technology with deep reinforcement learning (DRL) has emerged as a transformative approach in the realm of smart grid management. This abstract explores the intersection of these two domains, focusing on their collective potential to enhance energy efficiency through optimized resource allocation in smart grids. Smart grids represent a pivotal innovation in the modern energy landscape, offering a dynamic platform for the integration of renewable energy sources, efficient load management, and real-time grid monitoring. However, the complex and ever-changing nature of smart grids poses challenges in effectively allocating energy resources to meet diverse consumer demands while minimizing waste. Deep reinforcement learning, a subset of artificial intelligence, harnesses the power of IoT-connected devices and sensors to create adaptive, data-driven resource allocation strategies. The adaptability of DRL models to evolving grid conditions and consumer preferences enables dynamic resource allocation. This adaptability leads to more efficient energy resource utilization, waste reduction, and operational cost savings. Beyond cost benefits, energy-efficient resource allocation contributes significantly to sustainability objectives and grid resilience. The method consistently achieves the highest network throughput, ranging from 2700 units with 100 sensors to 2300 units with 500 sensors.Our model sheds light on the promising synergy between IoT technology and deep reinforcement learning for the purpose of resource allocation optimization in smart grids. The resulting improvements in energy efficiency promise economic advantages while simultaneously advancing environmental sustainability goals.