Considering the optimal input for global horizontal irradiance forecasting based on Informer

Accurate global horizontal irradiance (GHI) forecasting is significant for the stability and economy of power system operation. This paper proposes an advanced model Informer to predict GHI and selects Root Mean Square Error (RMSE) as the primary evaluation metric to calculate the error. Through discussing the Pearson correlation coefficient, chooses five strong correlation parameters and selects the optimal input through the experiments of 2 and 3 inputs. The performance of the Informer model is evaluated by comparing it to five other machine learning (ML) models being used as reference, and the performance improvement is over 99% under optimal input, proving the proposed model's superiority. Finally, the Informer's long series prediction ability is verified, and the results showed that Inforemer can efficiently complete long series prediction tasks without losing accuracy, which has high practical value.