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

• URL: http://arxiv.org/abs/2512.03114v1
• Date: Tue, 02 Dec 2025 10:16:14 GMT
• Title: Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data
• Authors: Srijani Mukherjee, Laurent Vuillon, Liliane Bou Nassif, Stéphanie Giroux-Julien, Hervé Pabiou, Denys Dutykh, Ionnasis Tsanakas,
• Abstract summary: The proposed model utilizes graph-based temporal relationships among key PV system parameters, including irradiance, module and ambient temperature to predict electrical power output.<n>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.
• Score: 0.11726720776908518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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