Load Forecasting of Power System Based on Integrated Sample System and Cloud Computing

被引：0
|
作者：
Wang Huizhong [1 ]
Liu Ke [1 ]
Zhu Hongyi [2 ]
机构：
[1] Lanzhou Univ Technol, Lanzhou 730050, Gansu Province, Peoples R China
[2] State Grid Gansu Elect Power Res Inst, Lanzhou 730050, Gansu Province, Peoples R China
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY, ENVIRONMENT AND CHEMICAL ENGINEERING | 2015年 / 23卷
关键词：
Integrated Sample System; Cloud computing; Particle swarm optimization; LSSVM;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
According to the characteristics of the short-term load forecasting, this paper established a integrated sample system. Through the analysis of various factors and load data to evaluate the effects of various factors on load forecasting, choosing the most appropriate forecast samples. PSO-LSSVM-Cloud model is established using Cloud computing technology to improve the efficiency of prediction. Finally, the actual data to establish PSO-LSSVM-Cloud model simulation comparison. Experimental results show that this load forecasting method has high forecasting precision and efficiency.
引用
收藏
页码：156 / 160
页数：5
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