Short-term power load forecasting using improved ant colony clustering

被引：4

作者：

Li Wei ^{[1
]}

Han Zhu-hua ^{[1
]}

机构：

[1] N China Elect Power Univ, Dept Business & Adm, Baoding 071001, Peoples R China

来源：

FIRST INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2007年

关键词：

power load forecasting; IACC; colony clustering; load curves;

D O I：

10.1109/WKDD.2008.30

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. Ant colony algorithms have been recently suggested for short-term load forecasting (STLF) by a large number of researchers. In this paper, an Improved Ant Colony Clustering (IACC) based on Ant Colony Algorithm was put forward. In IACC, each load data was represented by an ant, making use of the parallel optimization characteristics of ant colony algorithm and the ability of volatile quotient method to adaptively change the amount of information, with improvements have been made by changing the pheromone concentration on every path and enhancing the heuristic function to accelerate the searching process. Experiments and comparisons are done to show that the IACC is an efficient and effective approach, not only IACC increased the STLF accuracy, but also IACC is more exquisite to the similarity of load curve profile.

引用

页码：221 / 224

页数：4

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