The monthly electricity load forecast based on Composite model

被引：0
|
作者：
Xu Qifeng [1 ]
Wang Qiang [2 ]
Yao Zhilin [1 ]
Liu Shufen [1 ]
机构：
[1] Jilin Univ, Changchun, Jilin Province, Peoples R China
[2] SERI, Beijing, Peoples R China
来源：
2015 7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME) | 2015年
关键词：
Wavelet Transform; Stationary Process; AR model; Powerload forcasting;
D O I：
10.1109/ITME.2015.57
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the electric power area, Electric Power Load Forecasting (EPLF) is a fundamental process in the planning of monthly electricity production of electric power systems. In this paper, we discuss different kind of consumptions of various consumer groups. We apply Smooth Processing on the case without significant fluctuation after doing Wavelet Transform, then we predict with AR model, and get EPLF from the combination of electricity load of various cases. The method that we proposed is verified by history data, the result shows that it can archeieve accurate EPLF.
引用
收藏
页码：664 / 667
页数：4
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