Leveraging Predictive Maintenance for Photovoltaic Systems

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability.