Short Term Load Forecasting using Artificial Intelligence

被引：0

作者：

Luthuli, Qiniso W. ^{[1
]}

Folly, Komla A. ^{[1
]}

机构：

[1] Univ Cape Town, Dept Elect Engn, Cape Town, South Africa

来源：

2016 IEEE PES POWERAFRICA CONFERENCE | 2016年

关键词：

Artificial intelligence; artificial neural network; conventional method; 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：

This paper presents a comparative study of short-term load forecasting using Artificial Intelligence (AI) and the conventional approach. A feed-forward, multilayer artificial neural network (ANN) was employed to provide a 24-hour load demand forecast. In this model, historical data, weather information, day types and special calendar days were considered. The forecasted results using AI were compared with those of conventional method. From the simulations it is found that the maximum forecasting percentage error for AI is approximately 5.5% as opposed to 15.96% for the conventional approach.

引用

页码：129 / 133

页数：5