Title :
Neural network-based short term load forecasting for unit commitment scheduling
Author :
Methaprayoon, K. ; Lee, W.J. ; Didsayabutra, P. ; Liao, James ; Ross, Richard
Author_Institution :
Energy Syst. Res. Center, Texas Univ., Arlington, TX, USA
Abstract :
Today´s electric power industry is undergoing many fundamental changes due to the process of deregulation. In the new market environment, the power system operation will become more competitive. The utilities are required to perform optimal planning in order to operate their system efficiently. The accuracy of future load forecast becomes crucial. This paper presents the development of an artificial neural network-based short-term load forecasting (STLF) for unit commitment scheduling and resource planning. The network structures are carefully tuned to obtain satisfying forecast results according to the load characteristics of the target utility system. The result indicates that ANN forecaster provides more accurate result and can be modified to satisfy the target utility´s requirement.
Keywords :
load forecasting; neural nets; power generation scheduling; power system analysis computing; network structures tuning; neural network-based short term load forecasting; optimal planning; power industry deregulation; power system operation; resource planning; unit commitment scheduling; Artificial neural networks; Economic forecasting; Error correction; Fuels; IEEE members; Job shop scheduling; Load forecasting; Neural networks; Power system planning; Temperature;
Conference_Titel :
Industrial and Commercial Power Systems, 2003. 2003 IEEE Technical Conference
Print_ISBN :
0-7803-7771-0
DOI :
10.1109/ICPS.2003.1201499