AI-Driven Predictive Maintenance for Smart Grids
DOI:
https://doi.org/10.56472/ICCSAIML25-157Keywords:
Artificial Intelligence (AI), Predictive Maintenance, Smart Grids, Energy Loss Minimization, Carbon Footprint Reduction, Internet of Things (IoT), Anomaly Detection, Machine Learning, Renewable Energy Integration, Energy ManagementAbstract
The integration of Artificial Intelligence (AI) into smart grids has revolutionized the management and maintenance of power distribution systems. AI-driven predictive maintenance leverages real-time data analytics to forecast equipment failures, optimize maintenance schedules, and enhance grid reliability. This paper explores the application of AI in predictive maintenance within smart grids, emphasizing its role in minimizing energy losses and reducing carbon footprints. By analyzing historical and real-time data from Internet of Things (IoT) sensors, AI models can detect anomalies and predict potential failures, enabling proactive interventions. The study also discusses various AI techniques, such as machine learning and unsupervised learning models like autoencoders and isolation forests, highlighting their effectiveness in anomaly detection without the need for labeled data. Furthermore, the paper addresses the challenges of integrating renewable energy sources into the grid and how AI facilitates efficient energy management. The findings underscore the significance of AI in transforming traditional grids into intelligent, self-healing systems that are both energy-efficient and environmentally sustainable
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