A new prediction model of electricity load based on hybrid forecast engine

被引：16

作者：

Ghiasi, Mohammad ^{[1
,2
]}

Jam, Majid Irani ^{[3
]}

Teimourian, Milad ^{[4
,5
]}

Zarrabi, Houman ^{[6
]}

Yousefi, Nasser ^{[3
]}

机构：

[1] Islamic Azad Univ, Dept Elect Engn, Sci & Res Branch, Tehran, Iran

[2] Tehran Metro Operat Co, Tehran, Iran

[3] Islamic Azad Univ, Young Researchers & Elite Club, Ardabil Branch, Ardebil, Iran

[4] Islamic Azad Univ, Sama Tech & Vocat Training Coll, Parsabad Moghan Branch, Parsabad Moghan, Iran

[5] Islamic Azad Univ, Young Res & Elite Club, Germi Branch, Ardebil, Iran

[6] ICT Res Ctr, Tehran, Iran

来源：

INTERNATIONAL JOURNAL OF AMBIENT ENERGY | 2019年 / 40卷 / 02期

关键词：

Forecast engine; electricity load; neural network; optimisation;

D O I：

10.1080/01430750.2017.1381157

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, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.

引用

页码：179 / 186

页数：8

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