用时序图神经网络预测光伏发电并早期发现异常
Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data
- 构建光伏系统参数的时序图结构，捕捉动态关联
- 基于里昂屋顶实测数据，实现功率预测与异常检测
- 适合新能源运维和智能监控系统开发者参考
太阳能光伏系统快速普及，亟需先进方法保障运行效率。本研究提出一种基于时序图神经网络（Temporal GNN）的新方法，利用辐照度、组件与环境温度等关键参数的时序图关系，预测光伏输出功率并检测异常。模型基于法国里昂一处屋顶户外设施采集的数据，包含光伏组件的电力测量值和气象参数。该方法通过建模多变量间的动态依赖关系，提升了对发电性能的预测精度与异常识别能力。
原文摘要 · Abstract (English)
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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