Multi-feature short-term load forecasting based on stacking ensemble learning

Abstract

Short-term power load forecasting plays an important role in power system dispatching. To improve forecasting accuracy, a short-term load forecasting model based on stacking ensemble learning was proposed. Firstly, add effective multi-feature variables, and establishes a Stacking ensemble learning model for the load data and feature, which was ensembles by Light Gradient Boosting Machine (abbr. LightGBM) and eXtreme Gradient Boosting (abbr. XGBoost) for prediction. Finally, the comparison and experimental results show that the forecasting error of the proposed model is less than that of the comparative model.