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Neural network-based short term load forecasting for unit commitment scheduling

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5 Author(s)
Methaprayoon, K. ; Energy Syst. Res. Center, Texas Univ., Arlington, TX, USA ; Lee, W.J. ; Didsayabutra, P. ; Liao, J.
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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.

Published in:

Industrial and Commercial Power Systems, 2003. 2003 IEEE Technical Conference

Date of Conference:

4-8 May 2003

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