Price forecast by PSO-BP neural network model based on price-load correlation coefficient

被引：0
|
作者：
Li, Na [1 ]
Li, Yuxia [1 ]
Wang, Lixia [1 ]
Yang, Yagang [1 ]
机构：
[1] Xi'an University of Technology, Xi'an 710048, China
来源：
Shuili Fadian Xuebao/Journal of Hydroelectric Engineering | 2010年 / 29卷 / 01期
关键词：
Electric industry - Forecasting - Costs - Neural networks;
D O I：
暂无
中图分类号：
F [经济]; C [社会科学总论];
学科分类号：
02 ; 03 ; 0303 ;
摘要：
The price forecast accuracy of a BP neural network is often lowered due to the low learning efficiency caused by too many of non-related inputs. This paper develops a new method of using the price-load correlation coefficient as a condition to input the load to the model. The coefficient can be obtained through correlation analysis of the price and it is used in the PSO-BP neural network model to reduce the non-related inputs and improve the forecast accuracy. This new method is verified by a simulation using the data of Sichuan power market.
引用
收藏
页码：219 / 222
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