Short-term Load Forecasting of Local Power Grid Based on Support Vector Machine

被引：0

作者：

Hua, Jing ^{[1
]}

Xiong, Wei ^{[1
]}

Zhou, Yanping ^{[2
]}

机构：

[1] Honghe Coll, Coll Engn, Mengzi 610000, Yunnan, Peoples R China

[2] Kunming Power Supply Bur, Kunming 650051, Yunnan, Peoples R China

来源：

PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT, COMPUTER AND SOCIETY (EMCS 2017) | 2017年 / 61卷

关键词：

Short term load forecasting; SVM; Prediction accuracy; Normalize;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Short term load forecasting is an important basic work for power system planning and scheduling, short term load forecasting method based on SVM is adopted in this paper, through analyze the factors that influence the load forecasting and normalize the factors which influence the load forecasting. Using the historical load data of a city in Yunnan as the training data, the results show that the method can improve the prediction accuracy to a certain extent.

引用

页码：1851 / 1856

页数：6