Study of Artificial Neural Network Based Short Term Load Forecasting

被引：0
|
作者：
Webberley, Ashton [1 ]
Gao, David Wenzhong [1 ]
机构：
[1] Univ Denver, Dept Elect & Comp Engn, Denver, CO 80208 USA
来源：
2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES) | 2013年
关键词：
Load forecast; artificial neural network; electric load forecasting; smart grid;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
With more and more renewable energy integrated into the power grid and demand response in smart grid environment, electric load forecasting becomes more important. Accurate load forecasting facilitates better renewable energy integration and electricity market operation. Over the years, different load forecasting methods have been developed and applied. Multiple linear regression and artificial neural network based methods are well accepted by industries. This paper focuses on ANN-based method and provides detailed steps of load forecasting including data processing and neural network design.
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页数：4
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