Short-term load forecasting for the holidays using fuzzy linear regression method

Kyung Bin Song, Young Sik Baek, Dug Hun Hong, Gilsoo Jang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net(ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the shortterm load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.

Original languageEnglish
Title of host publication2005 IEEE Power Engineering Society General Meeting
Volume2
Publication statusPublished - 2005 Oct 31
Event2005 IEEE Power Engineering Society General Meeting - San Francisco, CA, United States
Duration: 2005 Jun 122005 Jun 16

Other

Other2005 IEEE Power Engineering Society General Meeting
CountryUnited States
CitySan Francisco, CA
Period05/6/1205/6/16

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Song, K. B., Baek, Y. S., Hong, D. H., & Jang, G. (2005). Short-term load forecasting for the holidays using fuzzy linear regression method. In 2005 IEEE Power Engineering Society General Meeting (Vol. 2)