Forecasting of Load Model Based On Typical Daily Load Profile and BP Neural Network

被引：0

作者：

Zhang, Rongsen ^{[1
]}

Qi, Guigang ^{[1
]}

Li, Canbing ^{[1
]}

Li, Long ^{[1
]}

Bao, Yiping ^{[1
]}

Zhu, Yusheng ^{[1
]}

机构：

[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China

来源：

FIFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2012): ALGORITHMS, PATTERN RECOGNITION AND BASIC TECHNOLOGIES | 2013年 / 8784卷

关键词：

load model forecasting; BP neural network; typical daily load profile; loads of power consuming-industries; model vector machine theory;

D O I：

10.1117/12.2014031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Load modeling is recognized as a difficult issue in field of power system digital simulation. The reliability of the simulation results depends on the veracity of the load model which will further affect power system planning and aid decision making. In order to increase the accuracy of the load model, the composite loads of power consuming-industries were classified by their industry attributes and the components of them were also analyzed in this paper. Then, the mathematical model of load composition is established on the basic of typical daily load profile and the identification algorithm developed by C language is used to identify the parameters of composite loads by choosing the data collected during the corresponding characteristic time period of the typical day. Based on the model vector machine theory and the parameters identified, the parameters of composite load model of power consuming-industries can be calculated by using the way of least square approximation. And the BP neural network was used to forecast the parameters of composite loads of power consuming-industries. Finally, an example shows the validity of the proposed scheme.

引用

页数：7

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