Solar energy is a strongly intermittent renewable energy source, which is affected by varied meteorological conditions, and thus produces arbitrary power outputs in photovoltaic (PV) power generation. Complex weather variations make it challenging to develop an efficient PV power forecasting method. In this study, a graph modeling method is proposed for short-term PV power prediction. Unlike many conventional machine-learning models, the proposed model is capable of evaluating interconnections among various meteorological input factors. This study details the design and operation of graph modeling, including graph construction, node feature construction, message transfer, and readout. An entire model is established consisting of spectral graph convolution, multiple graphical edges and a hierarchical output manner. The testing results suggest that the proposed multi-graph model outperforms other benchmark models in terms of accuracy under day-ahead forecasting cases. Besides, the single-graph model achieves a reduced cost of training time comparing to deep-learning benchmark models.

中文翻译：

---

基于多气象因素的光伏发电预测图建模

太阳能是一种强间歇性可再生能源，受各种气象条件的影响，因此在光伏（PV）发电中产生任意功率输出。复杂的天气变化使得开发有效的光伏功率预测方法具有挑战性。在这项研究中，提出了一种用于短期光伏功率预测的图建模方法。与许多传统的机器学习模型不同，所提出的模型能够评估各种气象输入因素之间的相互联系。本研究详细介绍了图建模的设计和操作，包括图构建、节点特征构建、消息传递和读出。建立了一个由谱图卷积、多条图边和分层输出方式组成的完整模型。测试结果表明，所提出的多图模型在日前预测情况下的准确性方面优于其他基准模型。此外，与深度学习基准模型相比，单图模型降低了训练时间的成本。