Peak load forecasting using hierarchical clustering and RPROP neural network

被引：0
|
作者：
Liu Jin [1 ]
Yu Feng [1 ]
Yu Jilai [1 ]
机构：
[1] Harbin Inst Technol, Dept Elect Engn, Harbin 150001, Peoples R China
来源：
2006 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION. VOLS 1-5 | 2006年
关键词：
hierarchical clustering; Pattern recognition; peak load forecasting; resilient back propagation;
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：
In this paper, an approach is proposed for the daily loads prediction during the peak period, which combines the feed-forward neural network (FNN) using the resilient back propagation (RPROP) algorithm with the hierarchical clustering (HC) method. The HC method could offer clustering sets on different layers in selecting daily samples as a peak load pattern. The proposed predicting method proves to be more accurate and more quickly converge of FNN in the peak load forecasting by the simulating results to an actual power grid in China.
引用
收藏
页码：1535 / +
页数：3
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