Power Load Forecasting in the Spring Festival Based on Feedforward Neural Network Model

被引：0

作者：

Ren ZhiChao ^{[1
]}

Ye Qiang ^{[1
]}

Wang Haiyan ^{[1
]}

Cheng Chao ^{[1
]}

Liang Yuan ^{[2
]}

机构：

[1] SGSERI, Chengdu, Sichuan, Peoples R China

[2] Chengdu Univ Technol, Coll Management Sci, Chengdu, Sichuan, Peoples R China

来源：

PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2017年

关键词：

the Spring Festival load; load forecasting; feedforward neural network;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The Spring Festival is an important festival for family reunion, when the crowd returns to their hometown together, causing transfer and concentration of the power load. Accurate load forecasting can effectively enhance reliability of power supply in the Spring Festival. In order to solve the problem of load forecasting during the Spring Festival in Sichuan Province, the t-test is used to analyze the change trend of load during the Spring Festival. Then, considering the randomness and non-linear relationship of power load, the predecessor model is used in other fields, the accuracy of the power load during the Spring Festival in Sichuan Province. It is indicated in the result that there are laws for change in load during this period, and that Feedforward Neural Network has good effect on forecasting of load, providing bases for power load dispatch.

引用

页码：2855 / 2858

页数：4

共 7 条

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