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: Contribution to journalArticle

294 Citations (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 short-term 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
Pages (from-to)96-101
Number of pages6
JournalIEEE Transactions on Power Systems
Volume20
Issue number1
DOIs
Publication statusPublished - 2005 Feb

Keywords

  • Fuzzy sets
  • Fuzzy systems
  • Load forecasting

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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