A temporal fusion transformer augmented GeoAI framework for estimating hourly land surface solar irradiation

Accurate estimation of land surface solar irradiation is critical for effective solar energy utilization and planning of solar photovoltaic planning. Although traditional machine learning methods have been demonstrated to estimate solar irradiation effectively, they face challenges in modeling over large regions, as well as lacking of ability to model spatial diversity and temporal dynamics of solar irradiation, and providing limited interpretability. To address these limitations, this study proposed a geospatial artificial intelligence framework augmented by Temporal Fusion Transformer for hourly estimation of land surface solar irradiation. As a case study in Australia, the results demonstrate superior performance with the coefficient of the determination, the mean absolute error, and Root Mean Square Error as high as 0.90, 0.25(kWh/m²), and 0.63(kWh/m²), showing improvements of 21.62–66.67 %, 78.37–85.98 %, and 62.81–73.25 %, respectively, compared to the benchmarks of other methods, including Support Vector Regression, Random Forest, Gradient Boosting Machine, AdaBoost, Long Short-Term Memory, Temporal Convolutional Network, ConvLSTM, Transformer, and Graph Neural Network. Furthermore, interpretability results of the model indicate that among the temporal variables, observed solar irradiation and clear sky solar irradiation significantly contribute to the model’s performance. The results show this framework enhanced accuracy and interpretability for solar irradiation estimation over large areas, providing valuable insights for future studies and supporting decision-making for developing the renewable energy industry.