Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer

The number of existing global solar radiation (GSR) observation stations is limited, and it is challenging to meet the demand for scientific research and production. Different forecasting horizons of solar radiation correspond to various applications. Therefore, it is critical to design realistic models to predict the GSR of varying sequence lengths. This study proposes a prediction model based on the analysis of the Pearson correlation between GSR and each input parameter and the establishment of different input combinations related to the result of Pearson analysis in four high-quality datasets. The proposed Informer model compares the results with five classical machine learning models on four datasets with R2, RMSE, and skill score (S) as evaluation metrics. This study examines the proposed model's prediction performance for five prediction lengths, four climate zones, three sampling frequencies, and two input types. The results showed that the Informer model performs well with the clearness index and pressure as the input. Besides, the RRMSE values are less than 10% under optimal input in long sequence forecasting. The findings suggested that the proposed advanced Informer model is a reliable alternative for GSR prediction due to its high predictive accuracy under diverse prediction lengths, sampling frequencies, climate zones, and the number of input parameters.

Automated summary

Due to the limited number of global solar radiation observation stations, it is important to design realistic models for predicting global solar radiation of varying sequence lengths. This study proposes a prediction model based on Pearson correlation analysis and different input combinations. The proposed model, Informer, outperforms classical machine learning models in terms of predictive accuracy.