• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2020-03 (Vol.69 No.3)
  4. 10.5370/KIEE.2020.69.3.460
XML PDF INFO REF

References

1 
G. Janazcek, L. Swift, 1993, Time Series Forecasting, Simulation, Simulation, Applications, Ellis HorwoodGoogle Search
2 
B. H. Ahn, H. R. Choi, H. C. LEE, 2015, Regional Long- Term Load Forecasting using SARIMA in South Korea, Journal of the Korea Academia-Industrial cooperation Society, Vol. 16, No. 12, pp. 8576-8584DOI
3 
M. Hayati, Y. Shirvany, 2007, Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region, IJECSE, Vol. 1, No. 2, pp. 121-125Google Search
4 
H. S. Hwang, J. S. Oh, 2009, Time Series Sock Prices Prediction Based On Fuzzy Model, Journal of The Korean Institute of Intelligent Systems, Vol. 16, pp. 589-694DOI
5 
O. Valenzuela, I. Rojas, F. Rojas, H. Pomares, L. J. Herrera, A. Guillen, L, Marquez, M. Pasadas, 2008, Hybridization of Intelligent techniques and ARIMA models for time series prediction, Fuzzy Sets and Systems, Vol. 159, pp. 821-845DOI
6 
P. Mandal, T. Senjyu, T. Funabashi, 2006, Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market, Energy Conversion and Management, Vol. 47, No. 15-16, pp. 2128-2142DOI
7 
J. M. Mendel, 2001, Uncertain Rule-based Fuzzy Logic System: Introduction and New Directions, Prentice-Hall, Upper Saddle River, NjGoogle Search
8 
C. S. Ting, 2006, Stability analysis and design of Takagi-Sugeno fuzzy system, Information Science, Vol. 176, pp. 2817-2845DOI
9 
G. Mahalakshmi, S. Sridevi, S. Rajaram, 2016, A Survey on Forecasting of Time Series Data, in Proc. of 2016 International Conference on Computing Technologies and Intelligent Data Engineering, pp. 1-8DOI
10 
T. Velmurugan, T. Santhanam, 2010, Design of Multiple Model Fuzzy Predictors using Preprocessing and its Application, European Journal of Scientific Research, Vol. 46, No. 3, pp. 320-330Google Search
11 
Y. K. Bang, C. H. Lee, 2011, Fuzzy Time Series Prediction using Hierarchical Clustering Algorithms, Exports Systems with Applications, Vol. 38, pp. 4312-4325DOI
12 
Y. K. Bang, C. H. Lee, 2009, Design of Multiple Model Fuzzy Predictors using Preprocessing and its Application, Trans. KIEE, Vol. 58, No. 1, pp. 178-180Google Search
13 
M. Sugeno, G. T. Kang, 1988, Structure Identification of Fuzzy Model, Fuzzy Sets and Systems, Vol. 28, pp. 15-33DOI
14 
L. X. Wang, J. M. Mendel, 1992, Generating fuzzy rules from numerical data, with applications, IEEE Trans. on Systems, Man, and Cybern., Vol. 22, No. 6, pp. 1414-1427Google Search
15 
D. J. Kim, C. H. Kim, 1997, Forecasting Time Series with Genetic Fuzzy Predictor Ensemble, IEEE Trans. on Fuzzy Systems, Vol. 5, pp. 523-535DOI
16 
https://datamarket.com/data/set/22pv.Google Search
17 
Y. S. Joo, 2003, Fuzzy System Modeling Using Genetic Algorithm and Rough Set Theory, M. S. Thesis, Dept. of Electrical and Electronic Eng., Kangwon Univ., KoreaGoogle Search