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Improve the unit commitment scheduling by using the neural network based short term load forecasting

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5 Author(s)
T. Saksornchai ; The University of Texas at Arlington ; Wei-Jen Lee ; K. Methaprayoon ; J. Liao
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Unit commitment scheduling of the utility company relies upon the forecast of the demand, demand pattern, availability and capacity of the generators, minimum/maximum up and down time of the generators, and heat rate. According to the experiences of a local utility company, the difference of the fuel cost can reach million dollars per day with different unit commitment scheduling. Accurate hour-ahead and day-ahead demand forecasting play important roles for proper unit commitment scheduling. This paper describes the procedure to improve the unit commitment scheduling by using the hour-ahead and day-ahead results from the newly developed neural network based short-term load forecasting program in the SCADA/EMS system. Comparison of field records is also provided.

Published in:

Industrial and Commercial Power Systems Technical Conference, 2004 IEEE

Date of Conference:

1-6 May 2004