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

  • Saksornchai T
  • Lee W
  • Methaprayoon K
 et al. 
  • 11

    Readers

    Mendeley users who have this article in their library.
  • 33

    Citations

    Citations of this article.

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)

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Titti Saksornchai

  • Wei Jen Lee

  • Kittipong Methaprayoon

  • James R. Liao

  • Richard J. Ross

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free