Deep Learning Approaches for IoT-Driven Smart Grid Control Using Holographic CNNs with Renewable Energy Integration

Abstract

The evolution of smart grids and IoT-driven substations has transformed modern power systems by enabling real-time monitoring, adaptive control, and efficient integration of renewable energy sources and electric vehicles. The increasing complexity of energy networks, coupled with fluctuating renewable generation and dynamic load demands, necessitates advanced intelligent frameworks for control and optimization. Deep learning techniques have emerged as powerful tools for analysing large-scale grid data, improving prediction accuracy, and enhancing system resilience. Recent studies demonstrate that IoT-based smart grid architectures leverage sensors, communication networks, and machine learning models to enable real-time energy management and predictive analytics. Deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and hybrid architectures have been widely applied for load forecasting, anomaly detection, and grid optimization. These models improve energy distribution efficiency and support adaptive decision-making in dynamic environments. Furthermore, optimization techniques such as reinforcement learning and evolutionary algorithms have been integrated with deep learning models to enhance resource allocation and energy routing. The integration of renewable energy sources and electric vehicles introduces additional challenges due to intermittency and bidirectional energy flow, which are effectively addressed through intelligent control mechanisms. Holographic convolutional neural networks represent an emerging approach, enabling advanced feature representation and visualization in IoT-driven systems. This review focuses on developments, highlighting key advancements, architectures, and challenges in IoT-based smart grid systems. It also identifies research gaps related to scalability, security, and real-time implementation. The integration of deep learning, optimization techniques, and IoT technologies is expected to play a critical role in developing next-generation intelligent energy systems.