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

TL;DR

This paper introduces a Temporal Graph Neural Network model that predicts PV system performance and detects anomalies by modeling temporal relationships among environmental and operational parameters.

Contribution

It presents a novel Temporal GNN approach specifically designed for PV system monitoring and early anomaly detection, integrating temporal relationships in the data.

Findings

Effective prediction of PV power output

Early detection of system anomalies

Improved monitoring accuracy

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.