A Hybrid Tree-Based Ensemble Learning Model for Day-Ahead Peak Load Forecasting

Abstract

Daily peak load forecasting (DPLF) is critical in smart grid applications for security analysis, unit commitment, and scheduling of outages and fuel supplies. Although excellent single machine learning methods using tree-based ensemble learning or deep learning have shown satisfactory performance for DPLF, there is still room for improvement. This study proposes a hybrid tree-based ensemble learning model, called HYTREM, for robust DPLF. We first collected two commercial buildings’ energy consumption data from publicly available datasets. We then performed data preprocessing, such as input variable configuration, for the HYTREM modeling. We divided both datasets into training and test sets and generated the prediction values of several tree-based ensemble learning models, such as gradient boosting machine, extreme gradient boosting, Cubist, and random forest (RF), for each set as novel input variables. We reconstructed datasets using the Boruta algorithm to select all the relevant features and built an online RF model trained on these datasets using time-series cross-validation for day-ahead DPLF. The experimental results showed that the HYTREM performed a better performance than tree-based ensemble and deep learning methods in building-level DPLF in terms of the mean absolute percentage error and normalized root mean square error.