Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting

Abstract

Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum load data of Queensland, Australia, are used for the research. The analysis of the results indicates that the accuracy of the XGBoost model significantly depends on the values of its hyperparameters. Using default hyperparameter values may lead to substantial errors in load forecasts, while selecting appropriate values for the GA to determine the optimal hyperparameters for the XGBoost model can significantly improve its accuracy.