IoT-Driven Smart Grid Control and Monitoring Using Holographic Convolutional Neural Networks for Renewable Energy Integration

Abstract

The transformation of conventional power systems into intelligent smart grids has been significantly accelerated by the integration of Internet of Things (IoT) technologies, renewable energy sources, and electric vehicles (EVs). These advancements demand advanced monitoring, control, and optimization techniques to ensure grid stability, efficiency, and resilience. This survey reviews methods and architectures for IoT-driven control and monitoring of substations and smart grids, with a focus on integrating renewable energy and EVs using Holographic Convolutional Neural Networks (HCNNs). IoT enables real-time data acquisition and communication among grid components, while artificial intelligence techniques such as deep learning and reinforcement learning enhance predictive analytics and decision-making. HCNN models provide improved multidimensional feature extraction, enabling efficient handling of complex smart grid data. The survey also examines key architectures, including edge computing, fog computing, and cloud-based systems, along with energy-efficient Wireless Sensor Network (WSN) integration. Challenges such as energy variability, cybersecurity threats, data heterogeneity, and computational complexity are discussed. The study highlights emerging trends such as digital twin systems, AI-driven energy management, and IoT-based automation. Finally, future research directions are presented to improve scalability, security, and energy efficiency in next-generation smart grid systems.