A new grid search algorithm based on XGBoost model for load forecasting

Ngoc Thanh Tran, Thanh Thi Giang Tran, Tuan Anh Nguyen, Minh Binh Lam

Abstract


XGBoost is a highly effective and widely used machine learning model and its hyperparameters take an important role on the performance of the model. This paper presents a new grid search (GS) algorithm for obtaining optimal hyperparameters of the XGBoost model based on the median values of their error loss. A benchmark method used to evaluate the proposed and original GS algorithms is introduced. Datasets with measured daily electricity demand load values of Ho Chi Minh City, Vietnam and Tasmania state, Australia are analyzed for the performance of both algorithms. The error metrics, mean squared errors (MSEs), of the proposed algorithm are found to be 2,282 MW and 501 MW that are smaller than those of original algorithms, which are 2,424 MW and 537 MW in case of Ho Chi Minh City and Tasmania state, respectively. These results then verify the accuracy of the proposed algorithm.

Keywords


Load forecasting; Machine learning; Mean square error; XGBoost

Full Text:

PDF


DOI: https://doi.org/10.11591/eei.v12i4.5016

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).