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A gradient boosting machine-based framework for electricity energy knowledge discovery

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成果类型:
期刊论文
作者:
Xie, Bingbing;Zhu, Chenliang;Zhao, Liang;Zhang, Jun
通讯作者:
Zhu, C.;Zhao, L.
作者机构:
[Xie, Bingbing; Zhang, Jun] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
[Zhu, Chenliang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
[Zhu, Chenliang] Hankou Univ, Sch Innovat & Qual Dev, Wuhan, Peoples R China.
[Zhao, Liang] Chongqing Univ, Inst Sci Res & Dev, Chongqing, Peoples R China.
通讯机构:
[Zhu, C.] S
[Zhao, L.] I
School of Information Management, China
Institute of Scientific Research and Development, China
语种:
英文
关键词:
gradient boosting machine1;energy prediction2;knowledge discovery3;electricity consumption4;framework5.
期刊:
Frontiers in Environmental Science
ISSN:
2296-665X
年:
2022
卷:
10
页码:
1031095
机构署名:
本校为其他机构
院系归属:
信息管理学院
摘要:
Knowledge discovery in databases (KDD) has an important effect on various fields with the development of information science. Electricity energy forecasting (EEF), a primary application of KDD, aims to explore the inner potential rule of electrical data for the purpose to serve electricity-related organizations or groups. Meanwhile, the advent of the information society attracts more and more scholars to pay attention to EEF. The existing methods for EEF focus on using high-techs to improve the experimental results but fail to construct an applicable electricity energy KDD framework. To comple...

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