PV solar anomaly detection using low-cost data logger and ANN algorithm

Abstract

This paper presents an innovative edge device architecture that significantly enhances solar energy management systems. By integrating advanced functionalities such as generation prediction, maintenance alerts, and solar anomaly detection, this architecture transforms solar energy management. Through edge computing, it enables real-time analysis and decision-making at the network edge. Leveraging machine learning algorithms and accurate predictive models, these edge devices provide precise energy generation forecasts, facilitating optimal energy utilization and strategic planning for stakeholders. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime, and enabling timely maintenance. This approach ensures uninterrupted energy generation, enhancing the reliability and efficiency of the entire monitoring system. The integration of these features within edge devices improves the performance and reliability of energy monitoring systems. Implementing this cutting-edge architecture empowers stakeholders to achieve superior energy management, substantial cost reductions, and unparalleled system reliability.