Electricity Load Forecasting Using an Ensemble of Optimally-Pruned and Basic Extreme Learning Machines

被引：0

作者：

Marwala, Lufuno ^{[1
]}

Twala, Bhekisipho ^{[1
]}

机构：

[1] Univ Johannesburg, Sch Elect & Elect Engn, Johannesburg, South Africa

来源：

2017 9TH IEEE-GCC CONFERENCE AND EXHIBITION (GCCCE) | 2018年

关键词：

forecasting; electricity load; neural networks; support vector machines; neuro-fuzzy systems; extreme learning machines; optimally-pruned extreme learning machines; DEMAND;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper the application of artificial intelligence on one-step ahead forecasting of electricity consumption is investigated. The total electricity consumption data sampled on a monthly basis (monthly consumption) from January 1985 to December 2011 in South Africa is used. Neural networks, Neuro-fuzzy systems, support vector machines and optimally pruned and basic extreme learning machines (ELM) were used to develop nonlinear ensemble models for forecasting and their performance is compared. It was found that extreme learning machines significantly outperform traditional techniques except for support vector machines.

引用

页码：604 / 609

页数：6

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