Power Investment Forecast Based on Classification and Regression Tree

被引：0

作者：

Zhou, Hua ^{[1
]}

Lin, Shuang-qing ^{[1
]}

Li, Ming-wei ^{[1
]}

Shao, Xiao ^{[1
]}

Zhou, Jian-jia ^{[1
]}

Deng, Yan ^{[1
]}

Tao, Qian ^{[2
]}

机构：

[1] State Grid Corp China, Sichuan Elect Power Co, Chengdu, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu, Sichuan, Peoples R China

来源：

2018 INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL, AUTOMATION AND ROBOTICS (ECAR 2018) | 2018年 / 307卷

关键词：

Classification and regression tree; Grey correlation analysis; Infrastructure investment;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

There are many factors affecting the scale of power network infrastructure investment, and the traditional grey theory model method does not consider multiple features at the same time on the forecast results. In this paper, we model the prediction of infrastructure investment as the classification and regression tree building hierarchical tree structure. The validity and reasonableness of the model are verified by the calculation experiment of power network infrastructure investment. Compared to the grey theory model, this method with higher accuracy takes into account a number of features that are highly relevant to infrastructure investment.

引用

页码：581 / 585

页数：5

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