Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention

With the rapid development of high-performance computing technology, data-driven models, especially deep learning models, are being used increasingly for solar radiation prediction. However, the characteristics of the black box model lead to a lack of interpretability in their prediction results. This limits the application of the model in final optimization scenarios (such as model predictive control), as operation managers might not fully trust models lacking explanatory results. In our study, models were proposed based on the prediction model of the recurrent neural network. We hope to improve the interpretability of the models through the design and improvement of the model structure, thereby increasing the credibility of the model results. The interpretability in time and spatial dependencies of the prediction process were studied by the attention mechanism and graph neural network, respectively. Our results showed that the deep learning model, with attention, could effectively shift the attention mechanism to adapt to varying prediction target hours. The graph neural network expresses the most relevant variables in the dataset related to solar radiation through a self-learning graph structure. The results showed that solar radiation is connected directly with month, hour, temperature, penetrating rainfall, water vapor pressure, and radiation time.

Automated summary

The study focuses on enhancing the interpretability of deep learning models through the design and improvement of model structure, investigating time and spatial dependencies of the prediction process using attention mechanisms and graph neural networks.