Prediction of Global Horizontal Irradiance Using an Explainable Data Driven Machine Learning Algorithms

Abstract

Estimating global horizontal irradiance (GHI) with a high level of accuracy and precision is very challenging due to the volatile climate parameters and location constraints. To overcome this challenge, several machine learning (ML)-based techniques such as Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Extra Trees (ET) are implemented to forecast the GHI. The first stage of model development is to select the optimal subset of features by using the variance inflation factor feature selection method. In the second stage, the selected features are fed into the ML models and trained. The predictive performance of the ML models is improved the result of removal of insignificant input features. The predictive accuracy of the ML models is compared and evaluated by performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). Conclusively, after feature selection it is seen that the ET algorithm outperforms the others because of its lowest MAE and RMSE value of 3.01 and 1.748, respectively, as compared to the other models, indicating its relevancy, legitimacy, and viability for the estimation of GHI. The higher R 2 value of 0.99 obtained by the ET model indicates that it is best fitted with the dataset. Additionally, optimal shapely additive explanation values have been used as feature attributions for determining the magnitude and direction of the impact of each feature on the outcome.