Short-term power load forecasting based on Least Squares Support Vector Machine optimized by Bare Bones Fireworks algorithm

被引：0

作者：

Lei, Caijia ^{[1
]}

Fang, Binghua ^{[1
]}

Gao, Hui ^{[1
]}

Jia, Wei ^{[1
]}

Pan, Wei ^{[1
]}

机构：

[1] Guangzhou Power Supply Bur Co Ltd, Guangzhou 550002, Guangdong, Peoples R China

来源：

PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018) | 2018年

关键词：

short-term power forecasting; least squares support vector machine; bare bones fireworks algorithm; parameters optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Short-term load forecasting is a fundamental work in power system, which is significant for the control and dispatch of power system. This paper proposes a least squares support vector machine (LSSVM) algorithm optimized by bare bones fireworks algorithm (BBFWA) to enhance the accuracy of short-term power load forecasting. The forecasting model is based on least squares support vector machine. Then, the parameters of LSSVM are optimized by BBFWA. Compared to the other algorithms, the proposed method can forecast the short-term power load effectively and cost less time to optimize the parameters of LSSVM.

引用

页码：2231 / 2235

页数：5

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