[1]章曙光 蔡庆生.电力负荷预测的CBR中权重向量的选取模型[J].计算机技术与发展,2007,(06):197-199.
　ZHANG Shu-guang,CAI Qing-sheng.Model of Choosing Weight Vector in CBR for Forecasting Electric Power Load[J].,2007,(06):197-199.
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电力负荷预测的CBR中权重向量的选取模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2007年06期
页码:
197-199
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Model of Choosing Weight Vector in CBR for Forecasting Electric Power Load
文章编号:
1673-629X（2007）06-0197-03
作者:
章曙光1; 2 蔡庆生2
[1]安徽建筑工业学院计算机与信息工程系[2]中国科学技术大学计算机科学与技术系
Author(s):
ZHANG Shu-guang;  CAI Qing-sheng
[1]Dept. of Computer and Information Eng., Anhui Inst. of Architecture[2]Dept. of Computer Sci. and Techn. ,Univ. of Sci. and Techn. of China
关键词:
基于范例的推理; 负荷预测; 权重向量
Keywords:
CBR;  load forecasting;  weight vector
分类号:
TP18
文献标志码:
A
摘要:
电力负荷预测是一个较为复杂的过程,由于影响负荷的因素较多,权重向量的选取较为困难,导致负荷预测的准确性较差。通过遗传算法选取合适的权重向量,在范例检索的过程中利用时间序列和组合属性对权重向量和预测结果进行进一步修正,使得负荷预测的精度大大提高,实验结果表明该模型具有有效性和实用性
Abstract:
Forecasting to electric power load is a complex process. There are many factors affect the load, and it is difficult to choose the weight vector, which lead to the worse veracity. The paper firstly chooses appropriate weight vector by genetic algorithms, then amends the vector and the result of forecasting through making use of time series and attribute- combined during the process of case matching, accordingly the precision is greatly improved. Furthermore, the result of experiments show the model has validity and practicability

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备注/Memo

备注/Memo:
安徽省教育厅自然科学项目（2006KJ066B）章曙光（1970-）,男,安徽合肥人,讲师,博士研究生,研究方向为人工智能和机器学习; 蔡庆生,教授,博士生导师,研究方向为人工智能和机器学习
更新日期/Last Update: 1900-01-01