DocumentCode :
2844665
Title :
Artificial neural networks applied to long-term electricity demand forecasting
Author :
Mamun, M.A. ; Nagasaka, Ken
Author_Institution :
Dept. of Electr; & Electron. Eng., Tokyo Univ. of Agric. & Technol., Japan
fYear :
2004
fDate :
5-8 Dec. 2004
Firstpage :
204
Lastpage :
209
Abstract :
The electric power demand in Japan has steadily increased and the load factor of total power system has decreased. 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 maximum demands for 2001 through 2015 are predicted to be elevated from 179.42 GW to 209.18 GW. The annual average rate of load growth seen per ten years until 2015 is about 1.39%.
Keywords :
electric current; load forecasting; power engineering computing; power system economics; radial basis function networks; total energy systems; RBFN; artificial neural networks; economic factors; electric load factor; electric power demand; long-term electricity demand forecasting; radial basis function network; total power system; Artificial neural networks; Demand forecasting; Economic forecasting; Load forecasting; Power demand; Power generation economics; Power system economics; Power systems; Radial basis function networks; Reflection; Economic Factors; Electric Load Demand; Long-term Load Fore-casting; Radial Basis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on
Print_ISBN :
0-7695-2291-2
Type :
conf
DOI :
10.1109/ICHIS.2004.27
Filename :
1410005
Link To Document :
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