Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model

Bagging–XGBoost algorithm based extreme weather identification and short-term load forecasting model

Accurate short-term load forecasting of distribution transformer in extreme weather will effectively assist power dispatching and enable safe and stable operation of power grid. Therefore, this paper proposes the Bagging–XGBoost algorithm based extreme weather identification and short-term load fore...

Full description

Bibliographic Details
Main Authors: Xuzhi Deng, Aoshuang Ye, Jiashi Zhong, Dong Xu, Wangwang Yang, Zhaofang Song, Zitong Zhang, Jing Guo, Tao Wang, Yifan Tian, Hongguang Pan, Zhijing Zhang, Hui Wang, Chen Wu, Jiajia Shao, Xiaoyi Chen
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:Energy Reports
Subjects:
Ensemble learning
Extreme weather identification
Mutual information
Short-term load forecasting
XGBoost
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722012124

Internet

http://www.sciencedirect.com/science/article/pii/S2352484722012124

Similar Items