Short-term power load probability density forecasting based on GLRQ-Stacking ensemble learning method

Abstract

The high penetration rate of distributed energy brings severe challenges to the dispatch and operation of power systems. Improving the accuracy of short-term power load forecasting can enhance the management efficiency of the system, and thus enhance the economic and social benefits of the system. To that end, gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and random forest (RF) are used as the base learners, and quantile regression long-short term memory (QRLSTM) is used as the meta-learner to construct a novel Stacking ensemble learning model (GLRQ-Stacking) in this paper. This is a novel heterogeneous ensemble framework, which cleverly combines Bagging and Boosting to achieve Stacking. Thereinto, more comprehensive forecast information is provided by QRLSTM. Afterwards, a density prediction method using Gaussian approximation of quantiles (GAQ) to modify kernel density estimation (KDE) is proposed. The reconstructed probability density curves are derived by modifying KDE. While unifying the values of metrics, the constructed prediction intervals (PIs) cover more real values. The accuracy and effectiveness of the framework are evaluated based on two German datasets from different seasons and one Electrician Mathematical Contest in Modeling (EMCM) data case. Through the comparison of the results, the superiority of the proposed ensemble framework and the modified density prediction method is verified, which could improve operations management of power systems.