Machine learning techniques for short-term load forecasting

被引：0

作者：

Becirovic, Elvisa ^{[1
]}

Cosovic, Marijana ^{[2
]}

机构：

[1] Publ Util Elektroprivreda Bosnia & Herzegovina, Sarajevo, Bosnia & Herceg

[2] Fac Elect Engn, Istocno Sarajevo, Bosnia & Herceg

来源：

PROCEEDINGS OF THE 2016 4TH INTERNATIONAL SYMPOSIUM ON ENVIRONMENTAL FRIENDLY ENERGIES AND APPLICATIONS (EFEA) | 2016年

关键词：

machine learning; short-term load forecasting;

D O I：

暂无

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：

Selection of an adequate tool for accurate short-term load forecasting task is becoming more important for electric utilities. Machine learning techniques are proving useful for short-term electricity load forecasting. In this paper we evaluate performance of several machine learning algorithms applied to electricity load datasets. We evaluated performance of SMOreg, and Additive regression algorithms for load forecasting using electricity consumption datasets. We also performed an Artificial Neural Networks (ANN) analysis on short-term load forecasting.

引用

页数：4

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