단기수요예측 알고리즘An Algorithm of Short-Term Load Forecasting
- Other Titles
- An Algorithm of Short-Term Load Forecasting
- Authors
- 송경빈; 하성관
- Issue Date
- Oct-2004
- Publisher
- 대한전기학회
- Keywords
- Load Forecasting; Fuzzy Linear Regression; General Exponential Smoothing; Temperature Sensitivity; Load Forecasting; Fuzzy Linear Regression; General Exponential Smoothing; Temperature Sensitivity
- Citation
- 전기학회논문지 A권, v.53, no.10-A, pp.529 - 535
- Journal Title
- 전기학회논문지 A권
- Volume
- 53
- Number
- 10-A
- Start Page
- 529
- End Page
- 535
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/20179
- ISSN
- 1229-2443
- Abstract
- Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.
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