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

  • Saksornchai T
  • Lee W
  • Methaprayoon K
 et al. 
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Abstract

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 a 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 supervisory control and data acquisition and energy management system. Comparison of field records is also provided.

Author-supplied keywords

  • Neural networks
  • Production cost
  • Short-term load forecast
  • Unit Commitment (UC)

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Authors

  • Titti Saksornchai

  • Wei Jen Lee

  • Kittipong Methaprayoon

  • James R. Liao

  • Richard J. Ross

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