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http://dx.doi.org/10.5370/KIEE.2017.66.12.1751

Electric Load Forecasting using Data Preprocessing and Fuzzy Logic System  

Bang, Young-Keun (Dept. of Electrical Engineering, Kangwon National University)
Lee, Chul-Heui (Dept. of Electrical and Electronic Engineering, Kangwon National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.66, no.12, 2017 , pp. 1751-1758 More about this Journal
Abstract
This paper presents a fuzzy logic system with data preprocessing to make the accurate electric power load prediction system. The fuzzy logic system acceptably treats the hidden characteristic of the nonlinear data. The data preprocessing processes the original data to provide more information of its characteristics. Thus the combination of two methods can predict the given data more accurately. The former uses TSK fuzzy logic system to apply the linguistic rule base and the linear regression model while the latter uses the linear interpolation method. Finally, four regional electric power load data in taiwan are used to evaluate the performance of the proposed prediction system.
Keywords
TSK fuzzy logic system; data preprocessing; linear interpolation; electric power load data;
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Times Cited By KSCI : 1  (Citation Analysis)
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