Short-term Load Forecasting Using XGBoost and the Analysis of Hyperparameters

被引：1
|
作者：
Oh J.-Y. [1 ]
Ham D.-H. [1 ]
Lee Y.-G. [1 ]
Kim G. [1 ]
机构：
[1] School of Electrical Engineering, Soongsil University
来源：
Transactions of the Korean Institute of Electrical Engineers | 2019年 / 68卷 / 09期
关键词：
Hyperparameter; Load forecasting; Machine Learning; XGBoost;
D O I：
10.5370/KIEE.2019.68.9.1073
中图分类号：
学科分类号：
摘要：
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. ©The Korean Institute of Electrical Engineers
引用
收藏
页码：1073 / 1078
页数：5
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