Artificial neural networks applied to long-term electricity demand forecasting

被引:0
|
作者
Al Mamun, M [1 ]
Nagasaka, K [1 ]
机构
[1] Tokyo Univ Agr & Technol, Dept Elect & Elect Engn, Koganei, Tokyo, Japan
来源
HIS'04: Fourth International Conference on Hybrid Intelligent Systems, Proceedings | 2005年
关键词
economic factors; electric load demand; long-term load fore-casting; radial basis function net-works;
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
The electric power demand in Japan has steadily increased and the load factor of total power system has de-creased. It is therefore very important to the utilities to have advance knowledge of their electrical load. One of the important points for forecasting the long-term load in Japan is to take into account the past and present economic situations and power demand. These points were considered in this study. The proposed Artificial Neural Network (ANN) that is Radial Basis Function Network (RBFN) has also showed that the changes in loads are a reflection of economy. Here, prediction of peak loads in Japan up to year 2015 is discussed using the RBFN and the maxi-mum demands for 2001 through 2015 are predicted to be elevated from 179.42GW to 209.18GW The annual average rate of load growth seen per ten years until 2015 is about 1.39%.
引用
收藏
页码:204 / 209
页数:6
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