Forecasting short-term electricity demand of Turkey by artificial neural networks

被引：0

作者：

Comert, Mustafa ^{[1
]}

Yildiz, Ali ^{[1
]}

机构：

[1] MERSIN Univ, Elekt Elekt Muhendisligi Bolumu, Mersin, Turkey

来源：

2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP) | 2018年

关键词：

Turkey; electricity demand estimation; artificial neural network; time series; GENETIC ALGORITHM APPROACH; ENERGY-CONSUMPTION; CLIMATIC CONDITIONS; GREY PREDICTION; PEAK-DEMAND; MODEL; REGRESSION; INDUSTRIAL; BUILDINGS; PROVINCE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the last decades, electricity demand of Turkey shows a deterministic rising which can be mathematically modelled by the help of population, meteorological and economic parameters. In this study, an artificial neural network model was developed by using only time dependent electrical demand series in monthly resolution. In the model, only time series were used as input in training, but not population, meteorological, and economical parameters. Forecasting of annual gross electricity energy demand was estimated succesfully by the model under 1% average error.

引用

页数：6

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