Deep Learning Techniques in IoT-Enabled Smart Grids Review Approach for Energy Management

The increasing proliferation of smart grids and the growing share of renewable energy sources call for innovative and intelligent approaches to energy distribution management. Conventional energy management techniques encounter significant limitations, including suboptimal energy allocation, elevated operational expenses, and limited adaptability to dynamic load variations within the network. This study introduces an advanced smart grid architecture that incorporates IoT-based sensors and a control mechanism powered by deep learning algorithms. By leveraging data from IoT devices and centralized databases, the proposed system enables continuous monitoring of grid parameters, supports real-time analytics, and facilitates adaptive and predictive decision-making. These capabilities contribute to enhanced energy distribution efficiency, reduced technical losses, and improved overall system reliability. Furthermore, the architecture ensures robust resource allocation, even under conditions of unforeseen failures of energy assets, including generation units, distribution infrastructure, or end-users. The system also supports accurate demand forecasting and contributes to maintaining grid stability. Through the integration of IoT technologies, deep learning models, and real-time data processing, the proposed intelligent energy management framework is well equipped to address the challenges of increasing energy demand and the variability inherent in renewable energy generation.