| Title |
Short-term Load Forecasting Using XGBoost and the Analysis of Hyperparameters |
| Authors |
오재영(Jae-Young Oh) ; 함도현(Do-Hyeon Ham) ; 이용건(Yong-Geon Lee) ; 김기백(Gibak Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.9.1073 |
| Keywords |
Load forecasting; Machine Learning; XGBoost; Hyperparameter |
| Abstract |
Accurate load forecasting is getting vital with social and economic development to secure electricity supply and minimize redundant electricity generation. The load forecasting is also essential for efficient power system operation. As machine learning techniques become popular due to the breakthroughs in the application of intelligent systems such as speech or image recognition, variety of machine learning algorithms have also been applied to predict electricity demand. For load forecasting, this paper employs XGBoost algorithm that has recently been receiving attention. To yield the maximum performance of the XGBoost model, we performed grid search method to find optimal hyperparameters of XGBoost. The effects of the XGBoost model's hyperparameters on the model are assessed and visualized. |