Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data

The rapid growth of solar photovoltaic (PV) systems necessitates advanced methods for performance monitoring and anomaly detection to ensure optimal operation. In this study, we propose a novel approach leveraging Temporal Graph Neural Network (Temporal GNN) to predict solar PV output power and detect anomalies using environmental and operational parameters. The proposed model utilizes graph-based temporal relationships among key PV system parameters, including irradiance, module and ambient temperature to predict electrical power output. This study is based on data collected from an outdoor facility located on a rooftop in Lyon (France) including power measurements from a PV module and meteorological parameters. The Temporal GNN model integrates Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) to capture spatial and temporal dependencies effectively. The model is trained to minimize Mean Squared Error (MSE) loss and is evaluated using Mean Absolute Error (MAE). Anomalies are identified by computing absolute errors and setting a threshold. The proposed framework achieves a MAE of 0.0707 on normalized output power prediction, outperforming traditional methods (e.g., Random Forests, SVMs) and advanced deep learning models (e.g., LSTMs) reported in recent literature, highlighting its potential to set a new benchmark for solar PV performance prediction and anomaly detection. The proposed method holds significant promise for real-world applications, providing actionable insights for maintenance and optimization in solar PV installations.