Power System Short-term Load Forecasting Based on Neural Network with Artificial Immune Algorithm

被引：0
|
作者：
Huang Yue [1 ]
Li Dan [2 ]
Gao Liqun
机构：
[1] Shenyang Ligong Univ, Sch Informat Sci & Engn, Shenyang 110168, Peoples R China
[2] Northeastern Univ Shenyang, Sch Informat Sci & Engn, Shenzhen 110004, Peoples R China
来源：
PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2012年
关键词：
artificial immune algorithm; neural network; power system; load forecasting;
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：
This paper offers one kind of improved artificial immune algorithm which takes different mutation strategy toward different unit that has various quality. This algorithm conducts self-adapt adjustment between mutation rate and crossover rate in order to achieve balance between search accuracy and search efficiency. This paper conducts DAIA-BPNN short-term power load forecast model based on DAIA algorithm. It uses DAIA algorithm to optimize the weight and threshold of BPNN while overcoming the blindness when selecting the weight and threshold of BPNN. The actual calculation example of the short-term power system load forecast shows that the method presented in this paper has higher forecast accuracy and robustness compared with artificial neural networks and regression analysis model.
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页码：844 / 848
页数：5
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