A hybrid XGBoost-RF residual learning model for energy consumption forecasting

Recently, energy consumption forecasting plays an important role in power systems, smart grids, and power generation networks. Given the complex nature and time dependence of electricity consumption data, applying machine learning techniques improves energy forecasting accuracy and performance. In this research, a hybrid XGBoost–Random Forest (XGBoost–RF) model is proposed to make better the forecasting accuracy. To improve the performance of the proposed model, a residual learning-based forecasting framework is presented to learn the remaining prediction errors. In the forecasting procedure, important prediction metrics such as Mean Absolute Error (MAE), and coefficient of determination R 2 are evaluated. The experimental results show that the proposed hybrid model achieves an MAE of 15.8683, Root Mean Square Error (RMSE) of 22.1588, R 2 of 0.985807, and Mean Absolute Percentage Error (MAPE) of 1.3118%. Compared with the base XGBoost model, the proposed model is able to decrease MAE by 11.19% and RMSE by 11.30%. In addition, the SHapley Additive Explanations (SHAP) method is used to examine the effect of the input features on the model predictions and improve model interpretability. The SHAP results show that temporal features and lag-based variables have an important role in energy consumption forecasting. In particular, lag_1 and lag_168 are identified as the most important features. The experimental results confirmed that the proposed hybrid model achieved lower prediction errors and a higher R 2 than the other evaluated models. These findings show that the proposed XGBoost-RF residual learning approach can improve energy consumption forecasting accuracy while providing useful information about the important forecasting features.