Title :
The local power demand estimation based on artificial neural network technique
Author :
Kiliç, O. ; Attar, P. ; Yumurtaci, R. ; Tanriöven, M.
Author_Institution :
Dept. of Electr. Eng., Yildiz Univ., Istanbul, Turkey
Abstract :
The demand to electrical energy increases day by day. It is very important to reflect this increasing demand accurately to power plant planning. ANN technique can be effectively used in load forecasting. In this paper, ANN load forecasting is performed by using some nonlinear input parameters such as temperature, humidity, rain conditions. Real electrical data obtained for the national grid and meteorological parameters are used in the presented application
Keywords :
load forecasting; neural nets; power system analysis computing; artificial neural network; electrical data; electrical energy; humidity; load forecasting; local power demand estimation; meteorological parameters; national grid; nonlinear input parameters; power plant planning; rain; temperature; Artificial neural networks; Load forecasting; Neural networks; Neurons; Power demand; Power generation; Power generation planning; Power system modeling; Power system planning; Predictive models;
Conference_Titel :
Electrotechnical Conference, 1998. MELECON 98., 9th Mediterranean
Conference_Location :
Tel-Aviv
Print_ISBN :
0-7803-3879-0
DOI :
10.1109/MELCON.1998.699376
