Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data
This addresses performance monitoring for solar PV systems, but appears incremental as it applies an existing method (Temporal GNN) to a new domain-specific dataset.
The study tackled the problem of monitoring solar photovoltaic (PV) systems by proposing a Temporal Graph Neural Network to predict power output and detect anomalies using environmental data, achieving unspecified results without concrete numbers.
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.