Hybrid load forecasting method with analysis of temperature sensitivities

Kyung Bin Song, Seong Kwan Ha, Jung Wook Park, Dong Jin Kweon, Kyu Ho Kim

Research output: Contribution to journalArticlepeer-review

106 Citations (Scopus)


Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. Many techniques for load forecasting, which are, for example, multiple linear regression, stochastic time series, Kalman filter, expert system, and computational intelligences such as fuzzy systems and artificial neural networks, have been investigated so far. This paper proposes a novel hybrid load forecasting algorithm, which combines the fuzzy linear regression method and the general exponential smoothing method with the analysis of temperature sensitivities. The fuzzy linear regression method is used to consider the lower load-demands in weekends and Monday than on weekdays. The normal load of weekdays is forecasted by the general exponential smoothing method. Moreover, the temperature sensitivities are used to improve the accuracy of the load forecasting with the relation to the daily load and temperature. The test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

Original languageEnglish
Pages (from-to)869-876
Number of pages8
JournalIEEE Transactions on Power Systems
Issue number2
Publication statusPublished - 2006 May

Bibliographical note

Funding Information:
Manuscript received June 20, 2005; revised September 29, 2005. This work was supported by the Soongsil University Research Fund. Paper no. TPWRS-00364-2005. K.-B. Song is with the Department of Electrical Engineering, Soongsil University, Seoul, Korea (e-mail: kbsong@ssu.ac.kr). S.-K. Ha is with the Korea Power Co., LTD, Boryeong, Korea. J.-W. Park is with the School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. D.-J. Kweon is with the Korea Electric Power Research Institute (KEPRI), Daejeon, Korea. K.-H. Kim is with the Department of Electrical Engineering, Ansan College of Technology, Ansan, Korea. Digital Object Identifier 10.1109/TPWRS.2006.873099

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Hybrid load forecasting method with analysis of temperature sensitivities'. Together they form a unique fingerprint.

Cite this