Photovoltaic Prognostics Using Neural Network Performance Prediction

The system performs preliminary actions by training neural network models during an initial period when the PV system is operating normally. These trained models then predict expected performance metrics during subsequent operation, enabling the system to detect faults before they occur by comparing actual performance against predicted performance. This preliminary modeling action transforms the system from reactive fault detection to proactive fault prediction.

The system implements feedback by continuously comparing actual performance metrics against predicted metrics generated by the neural network models. When deviations exceed predefined thresholds, the system generates alerts and can trigger remedial actions. This closed-loop feedback mechanism enables real-time monitoring and prediction, allowing the system to respond to performance deviations before they result in significant energy losses or system failures.

The system employs self-service by utilizing the PV system's own operational data to train and operate neural network models that predict its own performance. The system monitors its own metrics, compares them against predictions, and autonomously generates alerts or triggers remedial actions when deviations are detected. This self-monitoring and self-diagnosis capability reduces energy losses from faults without requiring extensive external monitoring infrastructure.

The system replaces complex mechanical or electronic monitoring hardware with software-based neural network models that run on existing computational infrastructure. Instead of adding numerous physical sensors and monitoring devices, the invention uses algorithmic models processed through standard computing systems, thereby reducing energy losses while minimizing increases in physical system complexity.