Towards the Use of AI, Internet of Things, and Knowledge Representation for Smart Grid Management, Failure Prediction, and Load Balancing

Abstract

The integration of the Internet of Things (IoT) and semantic web technologies is transforming smart grid management, enabling real-time monitoring and predictive analytics for efficient energy distribution. This paper proposes a comprehensive AI-based Semantic IoT Smart Grid System that addresses the growing challenges of unpredictable energy demand driven by external factors such as extreme weather conditions, large-scale social events, and economic fluctuations. The system utilizes a network of IoT sensors to collect real-time data on energy consumption, grid performance, and environmental factors. This data is then enriched and integrated using semantic web technologies, including ontologies and Semantic Web Rule Language (SWRL) rules. The system employs a Long Short-Term Memory (LSTM) neural network to accurately forecast energy demand by analyzing historical and real-time data, incorporating external factors such as weather patterns, social events, and economic conditions. The integration of SWRL rules enables automated decision-making and grid optimization. The rules are designed to detect potential risks, such as transformer overloads, and trigger automatic actions to redistribute energy loads across the grid, preventing overloads and optimizing energy distribution. The system's scalable and flexible architecture ensures that it can handle large datasets and rapidly changing grid conditions, adapting to unexpected demand surges or sudden shifts in weather patterns. By combining IoT, semantic web technologies, and machine learning, the system enables energy companies to maintain grid stability, optimize energy distribution, and proactively prevent overloads, ensuring reliable and efficient energy delivery.