Electrical load forecasting using hybrid of extreme gradient boosting and light gradient boosting machine

Code

• github.com/jovialniyo93/electrical_load_forecasting
  tf★ 1

Abstract

Ensemble learning methods have been used to improve performance accuracy through bias-variance trade-off techniques. However, there is still room to improve. This paper proposes an ensemble model to forecast the electrical load behavior based on a hybrid of Extreme Gradient Boosting (XGBoost) and Light gradient boosting machine (LGBM). Extreme gradient boosting (XGBoost), a Light gradient boosting machine (LGBM) and a hybrid of XGBoost and LGBM models are trained, evaluated, and compared. The experiments show that the proposed model outperforms other methods by reducing more than 1% in mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), and mean absolute error (MAE). The dataset from the Pennsylvania-New Jersey-Maryland interconnection power grid was used to validate the evolutionary capability of the proposed method and the finding of optimal accuracy of the model.

Tasks

Reproductions