Hybrid load forecasting method with analysis of temperature sensitivities

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

doi:10.1109/TPWRS.2006.873099

Hybrid load forecasting method with analysis of temperature sensitivities

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

102 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	869-876
Number of pages	8
Journal	IEEE Transactions on Power Systems
Volume	21
Issue number	2
DOIs	https://doi.org/10.1109/TPWRS.2006.873099
Publication status	Published - 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

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10.1109/TPWRS.2006.873099

Cite this

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abstract = "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.",

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Hybrid load forecasting method with analysis of temperature sensitivities

Abstract

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