Short-Term Bus Load Matching and Forecasting Model

被引：0

作者：

Li, Ran ^{[1
]}

Sun, Chenjun ^{[2
]}

Liu, Yang ^{[1
]}

Peng, Lilin ^{[1
]}

Zeng, Ming ^{[1
]}

机构：

[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China

[2] State Grid Hebei Elect Power Co, Shijiazhuang 050000, Hebei Province, Peoples R China

来源：

PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE & APPLICATION TECHNOLOGY (ICCIA 2016) | 2016年 / 56卷

关键词：

Short-term bus load forecasting; Least squares support vector machine (LSSVM); Matching model; Subtractive clustering;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Bus load forecasting is the foundation to make power grid to be secure, economic and efficient, and also is the concrete measure and effective way to promote national energy saving and emissions reduction. Based on daily load curve of typical areas, and by means of subtractive clustering, the regional bus load was classified and bus load matching model was constructed. Furthermore, bus load forecasting model with the least squares support vector machine model (LSSVM) was put forward. Finally, the validity of load matching and forecasting model was verified by numerical examples.

引用

页码：206 / 210

页数：5

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