Title :
Electricity price short-term forecasting using artificial neural networks
Author :
Szkuta, B.R. ; Sanabria, L.A. ; Dillon, T.S.
Author_Institution :
Appl. Comput. Res. Inst., La Trobe Univ., Melbourne, Vic., Australia
fDate :
8/1/1999 12:00:00 AM
Abstract :
This paper presents the system marginal price (SMP) short-term forecasting implementation using the artificial neural networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with backpropagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP
Keywords :
backpropagation; multilayer perceptrons; neural nets; power system analysis computing; power system economics; tariffs; Australia; Victoria; artificial neural nets; backpropagation; deregulated power systems; electricity price short-term forecasting; system marginal price; three-layered ANN paradigm; training; Artificial neural networks; Computer networks; Economic forecasting; Electricity supply industry; Electricity supply industry deregulation; Forward contracts; Load forecasting; Power generation; Power system modeling; Power systems;
Journal_Title :
Power Systems, IEEE Transactions on
