Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
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
Page pp.1073-1078
ISSN 1975-8359
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.